From e122dd5d542f7b5e04ea0a6b1cf1e2319c3b36c5 Mon Sep 17 00:00:00 2001 From: Jan Meis Date: Thu, 23 Nov 2023 13:42:56 +0100 Subject: [PATCH] try to fix pkgdown again --- docs/404.html | 128 - docs/articles/index.html | 106 - docs/articles/maintenance_guide.html | 173 - .../header-attrs-2.6/header-attrs.js | 12 - .../header-attrs-2.7/header-attrs.js | 12 - .../header-attrs-2.8.6/header-attrs.js | 12 - docs/articles/other_software_comparison.html | 18176 ---------------- .../header-attrs-2.6/header-attrs.js | 12 - .../header-attrs-2.7/header-attrs.js | 12 - .../header-attrs-2.8.6/header-attrs.js | 12 - docs/articles/test_choice_tree_pdf.pdf | Bin 64654 -> 0 bytes docs/articles/usage_guide.html | 4370 ---- .../header-attrs-2.6/header-attrs.js | 12 - .../header-attrs-2.7/header-attrs.js | 12 - .../header-attrs-2.8.6/header-attrs.js | 12 - docs/articles/validation_statement.html | 158 - .../header-attrs-2.6/header-attrs.js | 12 - .../header-attrs-2.7/header-attrs.js | 12 - .../header-attrs-2.8.6/header-attrs.js | 12 - 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- - - - - - -Page not found (404) • DescrTab2 - - - - - - - - - - - - -
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Page not found (404)

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- -Content not found. Please use links in the navbar. - -
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-

Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

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-

Site built with pkgdown 2.0.2.

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- - - - - - - - diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index f3f3a1b..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,106 +0,0 @@ - -Articles • DescrTab2 - - -
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Articles

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All vignettes

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Usage guidance
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Test choice tree (PDF)
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Comparison with other software
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Validation statement
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Maintenance guidance
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Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

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Site built with pkgdown 2.0.2.

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- - - - - - - - diff --git a/docs/articles/maintenance_guide.html b/docs/articles/maintenance_guide.html deleted file mode 100644 index 1c258d3..0000000 --- a/docs/articles/maintenance_guide.html +++ /dev/null @@ -1,173 +0,0 @@ - - - - - - - -Maintenance guidance • DescrTab2 - - - - - - - - - - - -
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Maintenance guidance

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2021-10-08

- - Source: vignettes/maintenance_guide.Rmd - - -
- - - -
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-Intoduction

-

DescrTab2 is a powerful package with vast customization options. With this, unfortunately, comes code that has to deal with quite a bit of special cases an exceptions. This document aims to describe the flow of control of the DescrTab2 package, so that future generations may continue development and successfully fix potential bugs.

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-
-

-Flow of control

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-descr

-

The user interfaces mostly with the descr function. descr does all the calculations, i.e. the evaluation of the summary statistics on the data (mean, sd, median, etc. for continuous variables and counts for categorical variables) and the calculation of statistical tests. For this descr calls the descr_cat and descr_cont functions, which evaluate the list of summary statistics on the data. descr_cat then calls test_cat and descr_cont calls test_cont, which calculate appropriate statistical tests. A detailed description for the choice of test can be read in the “Test choice” vignette.

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descr returns a DescrList object, which is basically a named list containing all calculation results and the formatting options.

-
-
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-print

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To turn a DescrList object into pretty output, the object has to be passed to the print function. print is a generic function. This means that if a DescrList object is passed to print, the specialized print.DescrList function will be invoked automatically.

-
-

-Preprocessing

-

Since the proper output format is highly document type dependent, print.DescrList creates output in two steps. The first step is independent of the output format: The creation of a DescrPrintObj by calling the create_printObj function.

-

In this function, proper formatting is applied to the results in the DescrList and the formatted values are saved inside a tibble. Formatting in this case means converting numbers to characters, reducing the number of decimal digits, combining variables like “Q1” and “Q3” into “Q1 - Q3”, formatting small p values to display as “<0.001” and adding “%” values to categorical variables.

-

Somewhat of an exception is the case printFormat="numeric". Here, numbers are not converted characters and consequently very little formatting can be applied.

-

The formatting in create_printObj is done by iterating over all variables in the DescrList object and creating an appropriate sub-table by calling one of create_numeric_subtable.cat_summary, create_numeric_subtable.cont_summary, create_character_subtable.cat_summary or create_character_subtable.cont_summary. Whether create_numeric_subtable or create_character_subtable is called is determined by the printFormat option (all options lead to create_character_subtable except printFormat="numeric). Whether .cat_summary or .cont_summary is called depends on the type of variable. The sub-tables are then concatenated to a master table.

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-Postprocessing

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The DescrPintObj is the transformed into appropriate output format by calling one of print_tex, print_html, print_word, print_console or print_numeric.

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print_console basically prints the tibble that is produces by create_printObj using a slightly modified version of the default method for printing tibbles.

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print_numeric basically prints the tibble produces by create_printObj if printFormat="numeric" was specified.

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print_tex and print_html use kableExtra to convert the tibble from create_printObj into raw tex or html output. Some special formatting has to be applied to these outputs to accomodate for superscripts and to escape special LaTeX characters.

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print_word produces a flextable object from the tibble returned by create_printObj. flextables play relatively nicely with word.

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Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

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Site built with pkgdown 1.6.1.

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- - - - - - diff --git a/docs/articles/maintenance_guide_files/header-attrs-2.6/header-attrs.js b/docs/articles/maintenance_guide_files/header-attrs-2.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/maintenance_guide_files/header-attrs-2.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/maintenance_guide_files/header-attrs-2.7/header-attrs.js b/docs/articles/maintenance_guide_files/header-attrs-2.7/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/maintenance_guide_files/header-attrs-2.7/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/maintenance_guide_files/header-attrs-2.8.6/header-attrs.js b/docs/articles/maintenance_guide_files/header-attrs-2.8.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/maintenance_guide_files/header-attrs-2.8.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/other_software_comparison.html b/docs/articles/other_software_comparison.html deleted file mode 100644 index 70d9df5..0000000 --- a/docs/articles/other_software_comparison.html +++ /dev/null @@ -1,18176 +0,0 @@ - - - - - - - -Comparison with other software • DescrTab2 - - - - - - - - - - - -
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Comparison with other software

- -

2021-10-08

- - Source: vignettes/other_software_comparison.Rmd - - -
- - - -
-

-Introduction

-

In this document, we compare various DescrTab2 tests and descriptive statistics with their SAS equivalents.

-

These comparisons are not automated. The user may check if the results presented within this document are according to the users preferences.

-

The datasets used in these comparisons are taken mostly from the ?help pages of the underlying test functions used within DescrTab2. For the SAS comparisons, these datasets are then written to .csv format and read into SAS with help of the ?foreign package.

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The origin of these datasets is described in the respective sections.

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-A note about the layout

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This document is created by including in the .html SAS output. Unfortunately, this has ugly side effects for the formatting of this document, but everything should still be readable.

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-
-

-Wilcoxon one-sample signed-rank test

-

Dataset origin: ?wilcox.test (accessed on R-version 4.0.3).

-
-x <- c(1.83,  0.50,  1.62,  2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
-y <- c(0.878, 0.647, 0.598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)
-dat_wilcox.test_1_sample <- tibble(diff = x-y)
-
-descr(dat_wilcox.test_1_sample, test_options = c(nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Total -
-		 -
-p -
-
- -(N=9) - -
-diff -
-N - -9 - -0.039Wil1 -
-mean - -0.43 - -
-sd - -0.43 - -
-median - -0.49 - -
-Q1 - Q3 - -0.01 – 0.62 - -
-min - max - --0.15 – 1 - -
-Wil1 Wilcoxon’s one-sample signed-rank test -
-
-
-

-Mann-Whitney U test

-

Dataset origin: ?wilcox.test (accessed on R-version 4.0.3).

-
-x <- c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46)
-y <- c(1.15, 0.88, 0.90, 0.74, 1.21)
-group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)))
-dat_wilcox.test_2_sample <- tibble(var=c(x,y), group=group)
-
-descr(dat_wilcox.test_2_sample, "group", test_options = c(nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Trt -
-		 -
-Ctrl -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=10) - -(N=5) - -(N=15) - - -
-var -
-N - -10 - -5 - -15 - -0.254MWU - -HL CI -
-mean - -1.3 - -0.98 - -1.2 - - -[-0.15, 0.76] -
-sd - -0.44 - -0.2 - -0.4 - - -
-median - -1.4 - -0.9 - -1.1 - - -
-Q1 - Q3 - -0.83 – 1.6 - -0.88 – 1.1 - -0.83 – 1.5 - - -
-min - max - -0.73 – 1.9 - -0.74 – 1.2 - -0.73 – 1.9 - - -
-MWU Mann-Whitney’s U test -
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-
-

-Kruskal-Wallis one-way ANOVA

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Dataset origin: ?kruskal.test (accessed on R-version 4.0.3).

-
-x <- c(2.9, 3.0, 2.5, 2.6, 3.2) # normal subjects
-y <- c(3.8, 2.7, 4.0, 2.4)      # with obstructive airway disease
-z <- c(2.8, 3.4, 3.7, 2.2, 2.0) # with asbestosis
-group <- c(rep("Trt", length(x)), rep("Ctrl", length(y)), rep("Placebo", length(z)))
-dat_kruskal.test <- tibble(var=c(x,y,z), group=group)
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-descr(dat_kruskal.test, "group", test_options = c(nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-Variables -
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-Trt -
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-Ctrl -
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-Placebo -
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-Total -
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-p -
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- -(N=5) - -(N=4) - -(N=5) - -(N=14) - -
-var -
-N - -5 - -4 - -5 - -14 - -0.680KW -
-mean - -2.8 - -3.2 - -2.8 - -2.9 - -
-sd - -0.29 - -0.79 - -0.74 - -0.61 - -
-median - -2.9 - -3.2 - -2.8 - -2.8 - -
-Q1 - Q3 - -2.6 – 3 - -2.5 – 3.9 - -2.2 – 3.4 - -2.5 – 3.4 - -
-min - max - -2.5 – 3.2 - -2.4 – 4 - -2 – 3.7 - -2 – 4 - -
-KW Kruskal-Wallis’s one-way ANOVA -
- - -SAS Output - -
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The SAS System
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The NPAR1WAY Procedure
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Kruskal-Wallis Test
Chi-SquareDFPr > ChiSq
0.771420.6800
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-Friedman test

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Dataset origin: ?friedman.test (accessed on R-version 4.0.3).

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-RoundingTimes <-
-matrix(c(5.40, 5.50, 5.55,
-         5.85, 5.70, 5.75,
-         5.20, 5.60, 5.50,
-         5.55, 5.50, 5.40,
-         5.90, 5.85, 5.70,
-         5.45, 5.55, 5.60,
-         5.40, 5.40, 5.35,
-         5.45, 5.50, 5.35,
-         5.25, 5.15, 5.00,
-         5.85, 5.80, 5.70,
-         5.25, 5.20, 5.10,
-         5.65, 5.55, 5.45,
-         5.60, 5.35, 5.45,
-         5.05, 5.00, 4.95,
-         5.50, 5.50, 5.40,
-         5.45, 5.55, 5.50,
-         5.55, 5.55, 5.35,
-         5.45, 5.50, 5.55,
-         5.50, 5.45, 5.25,
-         5.65, 5.60, 5.40,
-         5.70, 5.65, 5.55,
-         6.30, 6.30, 6.25),
-       nrow = 22,
-       byrow = TRUE,
-       dimnames = list(1 : 22,
-                       c("Round Out", "Narrow Angle", "Wide Angle")))
-
-idx <- rep(1:22, 3)
-dat <- tibble(var = c(RoundingTimes[,1], RoundingTimes[,2], RoundingTimes[,3]),
-              group = c(rep("Round Out", 22), rep("Narrow Angle", 22), rep("Wide Angle", 22)))
-
-
-descr(dat, "group", test_options = list(nonparametric=TRUE, indices=idx, paired=TRUE))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Round Out -
-		 -
-Narrow Angle -
-		 -
-Wide Angle -
-		 -
-p -
-
- -(N=22) - -(N=22) - -(N=22) - -
-var -
-N - -22 - -22 - -22 - -0.004Frie -
-mean - -5.5 - -5.5 - -5.5 - -
-sd - -0.27 - -0.26 - -0.27 - -
-median - -5.5 - -5.5 - -5.4 - -
-Q1 - Q3 - -5.4 – 5.6 - -5.4 – 5.6 - -5.3 – 5.5 - -
-min - max - -5 – 6.3 - -5 – 6.3 - -5 – 6.2 - -
-Frie Friedman test -
- - -SAS Output - -
- - - -
The SAS System
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The FREQ Procedure
-

-

-

-

-
Summary Statistics for group by var
Controlling for indices
-

-

-
-
- ---- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Cochran-Mantel-Haenszel Statistics (Based on Rank Scores)
StatisticAlternative HypothesisDFValueProb
1Nonzero Correlation15.35710.0206
2Row Mean Scores Differ211.14290.0038
-
-
-

-

-
Total Sample Size = 66
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-

-
- - -
-
-

-Cochrans Q test

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Dataset origin: This is a replicate of the dataset from the SAS documentation of the Cochrane Q test from proc freq.

-
-d.frm <- DescTools::Untable(xtabs(c(6,2,2,6,16,4,4,6) ~ ., 
-    expand.grid(rep(list(c("F","U")), times=3))), 
-    colnames = LETTERS[1:3])
-
-# rearrange to long shape    
-d.long <- reshape(d.frm, varying=1:3, times=names(d.frm)[c(1:3)], 
-                  v.names="resp", direction="long")
-idx <- d.long$id
-dat <- d.long[, 1:2] %>% mutate(time=as.character(time), resp=as.character(resp))
-
-descr(dat, "time", test_options = list(indices=idx, paired=TRUE))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-A -
-		 -
-B -
-		 -
-C -
-		 -
-p -
-
- -(N=46) - -(N=46) - -(N=46) - -
-resp -
-F - -28 (61%) - -28 (61%) - -16 (35%) - -0.014CocQ -
-U - -18 (39%) - -18 (39%) - -30 (65%) - -
-CocQ Cochran’s Q test -
- - -SAS Output - -
- - - -
The SAS System
-
The FREQ Procedure
-
 
-
Summary Statistics for B by C
-
Controlling for A
-

-

-
-
- ----- - - - - - - - - - - - - - - - -
Cochran's Q, for A by B by C
Chi-SquareDFPr > ChiSq
8.470620.0145
-
-
-
-
- - -
-
-

-McNemars test

-

Dataset origin: ?mcnemar.test (accessed on R-version 4.0.3). Note that this dataset is not explicitly defined in ?mcnemar.test. It is constructed to reflect the cross table defined there.

-
-dat <- tibble::tibble(var = c(rep("Approve", 794), rep("Approve", 150), rep("Disapprove", 86), rep("Disapprove", 570),
-                      rep("Approve", 794), rep("Disapprove", 150), rep("Approve", 86), rep("Disapprove", 570)),
-              group= c(rep("first", 1600), rep("second",1600)))
-              
-descr(dat, "group", test_options = list(paired=TRUE, indices=c(1:1600, 1:1600)))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
-#> Warning in test_cat(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
-#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
-#> exact McNemar's test.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-first -
-		 -
-second -
-		 -
-p -
-		 -
-CI -
-
- -(N=1600) - -(N=1600) - - -
-var -
-Approve - -944 (59%) - -880 (55%) - -<0.001McN - -Prop. dif. CI -
-Disapprove - -656 (41%) - -720 (45%) - - -[0.0058, 0.076] -
-McN McNemar’s test -
-
-descr(dat, "group", test_options = list(paired=TRUE, exact=TRUE, indices=c(1:1600, 1:1600)))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-first -
-		 -
-second -
-		 -
-p -
-		 -
-CI -
-
- -(N=1600) - -(N=1600) - - -
-var -
-Approve - -944 (59%) - -880 (55%) - -<0.001eMcN - -Prop. dif. CI -
-Disapprove - -656 (41%) - -720 (45%) - - -[0.021, 0.059] -
-eMcN Exact McNemar’s test -
-
-dat <-
-  tibble::tibble(x = c(
-    rep("Approve", 794),
-    rep("Approve", 150),
-    rep("Disapprove", 86),
-    rep("Disapprove", 570)
-  ),
-  y = c(
-    rep("Approve", 794),
-    rep("Disapprove", 150),
-    rep("Approve", 86),
-    rep("Disapprove", 570)
-  ))
-
-mcnemar.test(dat$x, dat$y, correct = FALSE)
-#> 
-#>  McNemar's Chi-squared test
-#> 
-#> data:  dat$x and dat$y
-#> McNemar's chi-squared = 17.356, df = 1, p-value = 3.099e-05
- - - - - -SAS Output - -
- - - -
The SAS System
-
The FREQ Procedure
-

-

-
-
- - - -
-
- - - - - - -
- - - - - - - - - - - - -
Frequency
Percent
Row Pct
Col Pct
-
-		 -
- - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table of x by y
xy
ApproveDisapproveTotal
Approve - - - - - - - - - - - - -
794
49.63
84.11
90.23
 - - - - - - - - - - - - -
150
9.38
15.89
20.83
 - - - - - - - - - - - - -
944
59.00
 
 
Disapprove - - - - - - - - - - - - -
86
5.38
13.11
9.77
 - - - - - - - - - - - - -
570
35.63
86.89
79.17
 - - - - - - - - - - - - -
656
41.00
 
 
Total - - - - - - -
880
55.00
 - - - - - - -
720
45.00
 - - - - - - -
1600
100.00
-
-
-
-
-

-

-
Statistics for Table of x by y
-

- -

-
-
- ------ - - - - - - - - - - - - - - - - - -
McNemar's Test
Chi-SquareDFPr > ChiSqExact
Pr >= ChiSq
17.35591<.0001.000037159
-
-
-
-
-
- ------ - - - - - - - - - - - - - - - - -
Simple Kappa Coefficient
EstimateStandard
Error		95% Confidence Limits
0.69960.01800.66440.7348
-
-
-

-

-
Sample Size = 1600
-

-

-
- - -
-
-

-Chi-squared test

-

Dataset origin: “Gender-Party dataset”:?chisq.test (accessed on R-version 4.0.3). “a-b dataset”: selfmade.

-
-dat <- tibble(gender=c(rep("F",sum(c(762, 327, 468)) ), rep("M", sum( c(484, 239, 477)))),
-              party=c(rep("Democrat", 762), rep("Independent", 327), rep("Republican", 468),
-                      rep("Democrat", 484), rep("Independent", 239), rep("Republican", 477)))
-              
-descr(dat, "gender")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-F -
-		 -
-M -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=1557) - -(N=1200) - -(N=2757) - - -
-party -
-Democrat - -762 (49%) - -484 (40%) - -1246 (45%) - -<0.001chi2 - -
-Independent - -327 (21%) - -239 (20%) - -566 (21%) - - -
-Republican - -468 (30%) - -477 (40%) - -945 (34%) - - -
-chi2 Pearson’s chi-squared test -
-
-descr(dat)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Total -
-		 -
-p -
-
- -(N=2757) - -
-gender -
-F - -1557 (56%) - -<0.001chi1 -
-M - -1200 (44%) - -
-party -
-Democrat - -1246 (45%) - -<0.001chi1 -
-Independent - -566 (21%) - -
-Republican - -945 (34%) - -
-chi1 Chi-squared goodness-of-fit test -
-
-chisq.test(dat$gender, dat$party)
-#> 
-#>  Pearson's Chi-squared test
-#> 
-#> data:  dat$gender and dat$party
-#> X-squared = 30.07, df = 2, p-value = 2.954e-07
-chisq.test(table(dat$gender))
-#> 
-#>  Chi-squared test for given probabilities
-#> 
-#> data:  table(dat$gender)
-#> X-squared = 46.227, df = 1, p-value = 1.053e-11
-chisq.test(table(dat$party))
-#> 
-#>  Chi-squared test for given probabilities
-#> 
-#> data:  table(dat$party)
-#> X-squared = 252.68, df = 2, p-value < 2.2e-16
-
-dat <- tibble(
-  
-  a = factor(c(0,
-               0,
-               1,
-               1,
-               0,
-               0,
-               0,
-               0,
-               0,
-               0,
-               1)),
-  b = factor(c(1,
-               1,
-               1,
-               1,
-               1,
-               1,
-               1,
-               0,
-               0,
-               1,
-               0))
-
-)
-descr(dat, "b")
-#> Warning in (function (x, y = NULL, correct = TRUE, p = rep(1/length(x), : Chi-
-#> squared approximation may be incorrect
-#> Warning in stats::prop.test(table(var, group), correct = FALSE): Chi-squared
-#> approximation may be incorrect
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-0 -
-		 -
-1 -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=3) - -(N=8) - -(N=11) - - -
-a -
-0 - -2 (67%) - -6 (75%) - -8 (73%) - -0.782chi2 - -Prop. dif. CI -
-1 - -1 (33%) - -2 (25%) - -3 (27%) - - -[-0.7, 0.53] -
-chi2 Pearson’s chi-squared test -
- - -SAS Output - -
- - - -
The SAS System
-
The FREQ Procedure
-

-

-
-
- - ------ - - - - - - - - - - - - - - - - - - - - - - - -
genderFrequency PercentCumulative
Frequency		Cumulative
Percent
F155756.47155756.47
M120043.532757100.00
-
-
-
-
-
- ---- - - - - - - - - - - - - - - - - - -
Chi-Square Test
for Equal Proportions
Chi-Square46.2274
DF1
Pr > ChiSq<.0001
-
-
-

-

-
Sample Size = 2757
-

- -

-
-
- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
partyFrequency PercentCumulative
Frequency		Cumulative
Percent
Democrat124645.19124645.19
Independent56620.53181265.72
Republican94534.282757100.00
-
-
-
-
-
- ---- - - - - - - - - - - - - - - - - - -
Chi-Square Test
for Equal Proportions
Chi-Square252.6812
DF2
Pr > ChiSq<.0001
-
-
-

-

-
Sample Size = 2757
-

- -

-
-
- - - -
-
- - - - - - -
- - - - - - - - - - - - -
Frequency
Percent
Row Pct
Col Pct
-
-		 -
- - ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table of gender by party
genderparty
DemocratIndependentRepublicanTotal
F - - - - - - - - - - - - -
762
27.64
48.94
61.16
 - - - - - - - - - - - - -
327
11.86
21.00
57.77
 - - - - - - - - - - - - -
468
16.97
30.06
49.52
 - - - - - - - - - - - - -
1557
56.47
 
 
M - - - - - - - - - - - - -
484
17.56
40.33
38.84
 - - - - - - - - - - - - -
239
8.67
19.92
42.23
 - - - - - - - - - - - - -
477
17.30
39.75
50.48
 - - - - - - - - - - - - -
1200
43.53
 
 
Total - - - - - - -
1246
45.19
 - - - - - - -
566
20.53
 - - - - - - -
945
34.28
 - - - - - - -
2757
100.00
-
-
-
-
-

-

-
Statistics for Table of gender by party
-

- -

-
-
- - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
StatisticDFValueProb
Chi-Square230.0701<.0001
Likelihood Ratio Chi-Square230.0167<.0001
Mantel-Haenszel Chi-Square128.9797<.0001
Phi Coefficient 0.1044 
Contingency Coefficient 0.1039 
Cramer's V 0.1044 
-
-
-

-

-
Sample Size = 2757
-

-

-
-
-


-
- - - -
The SAS System
-
The FREQ Procedure
-

-

-
-
- - - -
-
- - - - - - -
- - - - - - - - - - - - -
Frequency
Percent
Row Pct
Col Pct
-
-		 -
- - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Table of a by b
ab
01Total
0 - - - - - - - - - - - - -
2
18.18
25.00
66.67
 - - - - - - - - - - - - -
6
54.55
75.00
75.00
 - - - - - - - - - - - - -
8
72.73
 
 
1 - - - - - - - - - - - - -
1
9.09
33.33
33.33
 - - - - - - - - - - - - -
2
18.18
66.67
25.00
 - - - - - - - - - - - - -
3
27.27
 
 
Total - - - - - - -
3
27.27
 - - - - - - -
8
72.73
 - - - - - - -
11
100.00
-
-
-
-
-

-

-
Statistics for Table of a by b
-

- -

-
-
- - ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
StatisticDFValueProb
Chi-Square10.07640.7823
Likelihood Ratio Chi-Square10.07450.7849
Continuity Adj. Chi-Square10.00001.0000
Mantel-Haenszel Chi-Square10.06940.7921
Phi Coefficient -0.0833 
Contingency Coefficient 0.0830 
Cramer's V -0.0833 
WARNING: 75% of the cells have expected counts less
than 5. Chi-Square may not be a valid test.
-
-
-
-
-
- ---- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Fisher's Exact Test
Cell (1,1) Frequency (F)2
Left-sided Pr <= F0.6606
Right-sided Pr >= F0.8485
  
Table Probability (P)0.5091
Two-sided Pr <= P1.0000
-
-
-

-

-
Sample Size = 11
-

-

-
- - -
-
-

-t-test

-

Dataset origin: ?t.test, which references ?sleep (accessed on R-version 4.0.3).

-
-dat <- sleep[, c("extra", "group")]
-              
-
-descr(dat[, "extra"])
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Total -
-		 -
-p -
-
- -(N=20) - -
-value -
-N - -20 - -0.003tt1 -
-mean - -1.5 - -
-sd - -2 - -
-median - -0.95 - -
-Q1 - Q3 - --0.05 – 3.4 - -
-min - max - --1.6 – 5.5 - -
-tt1 Student’s one-sample t-test -
-
-descr(dat, "group")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-1 -
-		 -
-2 -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=10) - -(N=10) - -(N=20) - - -
-extra -
-N - -10 - -10 - -20 - -0.079tt2 - -Mean dif. CI -
-mean - -0.75 - -2.3 - -1.5 - - -[-3.4, 0.21] -
-sd - -1.8 - -2 - -2 - - -
-median - -0.35 - -1.8 - -0.95 - - -
-Q1 - Q3 - --0.2 – 2 - -0.8 – 4.4 - --0.05 – 3.4 - - -
-min - max - --1.6 – 3.7 - --0.1 – 5.5 - --1.6 – 5.5 - - -
-tt2 Welch’s two-sample t-test -
-
-descr(dat, "group", test_options = list(paired=TRUE, indices=rep(1:10, 2)))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-1 -
-		 -
-2 -
-		 -
-p -
-		 -
-CI -
-
- -(N=10) - -(N=10) - - -
-extra -
-N - -10 - -10 - -0.003tpar - -Mean dif. CI -
-mean - -0.75 - -2.3 - - -[-2.5, -0.7] -
-sd - -1.8 - -2 - - -
-median - -0.35 - -1.8 - - -
-Q1 - Q3 - --0.2 – 2 - -0.8 – 4.4 - - -
-min - max - --1.6 – 3.7 - --0.1 – 5.5 - - -
-tpar Student’s paired t-test -
- - -SAS Output - -
- - - -
The SAS System
-
The UNIVARIATE Procedure
-
Variable: extra
-

-

-
-
- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Moments
N20Sum Weights20
Mean1.54Sum Observations30.8
Std Deviation2.01791972Variance4.072
Skewness0.45185739Kurtosis-0.7747436
Uncorrected SS124.8Corrected SS77.368
Coeff Variation131.033748Std Error Mean0.45122057
-
-
-
-
-
- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Basic Statistical Measures
LocationVariability
Mean1.54000Std Deviation2.01792
Median0.95000Variance4.07200
Mode-0.10000Range7.10000
  Interquartile Range3.45000
-
-

-

-
Note: The mode displayed is the smallest of 3 modes with a count of 2.
-

-

-
-
-
-
- ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Tests for Location: Mu0=0
TestStatisticp Value
Student's tt3.412965Pr > |t|0.0029
SignM4.5Pr >= |M|0.0636
Signed RankS67.5Pr >= |S|0.0048
-
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Quantiles (Definition 5)
LevelQuantile
100% Max5.50
99%5.50
95%5.05
90%4.50
75% Q33.40
50% Median0.95
25% Q1-0.05
10%-0.70
5%-1.40
1%-1.60
0% Min-1.60
-
-
-
-
-
- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Extreme Observations
LowestHighest
ValueObsValueObs
-1.623.420
-1.243.77
-0.234.416
-0.1154.619
-0.155.517
-
-
-
-
-
-


-
- - - -
The SAS System
-
The TTEST Procedure
-
 
-
Variable: extra
-

-

-
-
- ---- -------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
groupMethodNMeanStd DevStd ErrMinimumMaximum
1 100.75001.78900.5657-1.60003.7000
2 102.33002.00220.6332-0.10005.5000
Diff (1-2)Pooled -1.58001.89860.8491  
Diff (1-2)Satterthwaite -1.5800 0.8491  
-
-
-
-
-
- ---- -------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
groupMethodMean95% CL MeanStd Dev95% CL Std Dev
1 0.7500-0.52982.02981.78901.23053.2660
2 2.33000.89773.76232.00221.37723.6553
Diff (1-2)Pooled-1.5800-3.36390.20391.89861.43462.8077
Diff (1-2)Satterthwaite-1.5800-3.36550.2055   
-
-
-
-
-
- - ------ - - - - - - - - - - - - - - - - - - - - - - - -
MethodVariancesDFt ValuePr > |t|
PooledEqual18-1.860.0792
SatterthwaiteUnequal17.776-1.860.0794
-
-
-
-
-
- - ------ - - - - - - - - - - - - - - - - - - - -
Equality of Variances
MethodNum DFDen DFF ValuePr > F
Folded F991.250.7427
-
-
-
-
- - -
-
-

-F-test

-

Dataset origin: Modified version of ?datasets::npk. npk is used in ?aov (accessed on R-version 4.0.3).

-
-dat <- data.frame(
-  y = npk$yield,
-  P = ordered(gl(3, 24)),
-  N = ordered(gl(3, 1, 24))
-)
-              
-descr(dat[, c("y", "P")], "P")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-1 -
-		 -
-2 -
-		 -
-3 -
-		 -
-Total -
-		 -
-p -
-
- -(N=24) - -(N=24) - -(N=24) - -(N=72) - -
-y -
-N - -24 - -24 - -24 - -72 - ->0.999F -
-mean - -55 - -55 - -55 - -55 - -
-sd - -6.2 - -6.2 - -6.2 - -6.1 - -
-median - -56 - -56 - -56 - -56 - -
-Q1 - Q3 - -50 – 59 - -50 – 59 - -50 – 59 - -50 – 59 - -
-min - max - -44 – 70 - -44 – 70 - -44 – 70 - -44 – 70 - -
-F F-test (ANOVA) -
-
-descr(dat[, c("y", "N")], "N")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-1 -
-		 -
-2 -
-		 -
-3 -
-		 -
-Total -
-		 -
-p -
-
- -(N=24) - -(N=24) - -(N=24) - -(N=72) - -
-y -
-N - -24 - -24 - -24 - -72 - -0.346F -
-mean - -54 - -56 - -54 - -55 - -
-sd - -5.6 - -6.9 - -5.6 - -6.1 - -
-median - -56 - -55 - -56 - -56 - -
-Q1 - Q3 - -50 – 58 - -50 – 61 - -49 – 58 - -50 – 59 - -
-min - max - -44 – 62 - -49 – 70 - -46 – 63 - -44 – 70 - -
-F F-test (ANOVA) -
- - -SAS Output - -
- - - -
The SAS System
-
The ANOVA Procedure
-

-

-
-
- - ---- - - - - - - - - - - - - - - - -
Class Level Information
ClassLevelsValues
P31 2 3
-
-
-
-
-
- ---- - - - - - - - - - - -
Number of Observations Read72
Number of Observations Used72
-
-
-


-
- - - -
The SAS System
-
The ANOVA Procedure
-
 
-
Dependent Variable: y
-

-

-
-
- - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
SourceDFSum of SquaresMean SquareF ValuePr > F
Model20.0000000.0000000.001.0000
Error692629.09500038.102826  
Corrected Total712629.095000   
-
-
-
-
-
- ------ - - - - - - - - - - - - -
R-SquareCoeff VarRoot MSEy Mean
0.00000011.248746.17274954.87500
-
-
-
-
-
- - ------- - - - - - - - - - - - - - - - - -
SourceDFAnova SSMean SquareF ValuePr > F
P2000.001.0000
-
-
-
-
-
-


-
- - - -
The SAS System
-
The ANOVA Procedure
-

-

-
-
- - ---- - - - - - - - - - - - - - - - -
Class Level Information
ClassLevelsValues
N31 2 3
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-
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- ---- - - - - - - - - - - -
Number of Observations Read72
Number of Observations Used72
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The SAS System
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The ANOVA Procedure
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Dependent Variable: y
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- - ------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
SourceDFSum of SquaresMean SquareF ValuePr > F
Model279.70250039.8512501.080.3457
Error692549.39250036.947717  
Corrected Total712629.095000   
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- ------ - - - - - - - - - - - - -
R-SquareCoeff VarRoot MSEy Mean
0.03031611.076936.07846354.87500
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- - ------- - - - - - - - - - - - - - - - - -
SourceDFAnova SSMean SquareF ValuePr > F
N279.7025000039.851250001.080.3457
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- - -
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-Mixed model ANOVA

-

Dataset origin: Modified version of ?nlme::Orthodont. Orthodont is used in ?lme (accessed on R-version 4.0.3).

-
-dat <- nlme::Orthodont
-dat2 <- nlme::Orthodont[1:64,]
-dat2$Sex <- "Divers"
-dat2$distance <- dat2$distance + c(rep(0.1*c(1,4,3,2), 10), 0.1*rep(c(0.4,2,1.5, 2.3), 6) )
-dat2$Subject <- str_replace_all(dat2$Subject, "M", "D")
-dat <- bind_rows(dat, dat2)
-dat <- as_tibble(dat)
-
-
-descr(dat[, c("Sex", "distance")], "Sex", test_options = list(paired=TRUE, indices=dat$Subject))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Male -
-		 -
-Female -
-		 -
-Divers -
-		 -
-p -
-
- -(N=64) - -(N=44) - -(N=64) - -
-distance -
-N - -64 - -44 - -64 - -0.003MiAn -
-mean - -25 - -23 - -25 - -
-sd - -2.9 - -2.4 - -2.9 - -
-median - -25 - -23 - -25 - -
-Q1 - Q3 - -23 – 26 - -21 – 24 - -23 – 27 - -
-min - max - -17 – 32 - -16 – 28 - -17 – 32 - -
-MiAn Mixed model ANOVA -
- - -SAS Output - -
- - - -
The SAS System
-
The Mixed Procedure
-

-

-
-
- ---- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Model Information
Data SetWORK.RDATA
Dependent Variabledistance
Covariance StructureVariance Components
Subject EffectSubject
Estimation MethodREML
Residual Variance MethodProfile
Fixed Effects SE MethodModel-Based
Degrees of Freedom MethodSatterthwaite
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- - ---- - - - - - - - - - - - - - - - - - - - - - - -
Class Level Information
ClassLevelsValues
Sex3Divers Female Male
Subject43D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11 D12 D13 D14 D15 D16 F01 F02 F03 F04 F05 F06 F07 F08 F09 F10 F11 M01 M02 M03 M04 M05 M06 M07 M08 M09 M10 M11 M12 M13 M14 M15 M16
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-
-
- ---- - - - - - - - - - - - - - - - - - - - - - - - - - -
Dimensions
Covariance Parameters2
Columns in X4
Columns in Z per Subject1
Subjects43
Max Obs per Subject4
-
-
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-
- ---- - - - - - - - - - - - - - - - - - -
Number of Observations
Number of Observations Read172
Number of Observations Used172
Number of Observations Not Used0
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-
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-
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- ------ - - - - - - - - - - - - - - - - - - - - - - - - - -
Iteration History
IterationEvaluations-2 Res Log LikeCriterion
01838.60158118 
11823.173756280.00000000
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-
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- - - - - -
Convergence criteria met.
-
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-
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- ---- - - - - - - - - - - - - - - - - - - - - - - - -
Covariance Parameter Estimates
Cov ParmSubjectEstimate
InterceptSubject2.2123
Residual 5.6941
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-
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-
-
- ---- - - - - - - - - - - - - - - - - - - - - - -
Fit Statistics
-2 Res Log Likelihood823.2
AIC (Smaller is Better)827.2
AICC (Smaller is Better)827.2
BIC (Smaller is Better)830.7
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-
-
-
-
- - ------ - - - - - - - - - - - - - - - - - - - -
Type 3 Tests of Fixed Effects
EffectNum DFDen DFF ValuePr > F
Sex2406.690.0031
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-
-
-
- - -
-
-

-Boschloos test

-

Dataset origin: selfmade.

-

DescrTab2 uses the exact2x2::boschloo with option tsmethod=central to calculate p-values. There is no comparison for this option readily available.

-
-dat <- tibble(gender=factor(c("M", "M", "M", "M", "M", "M", "F", "F", "F", "F", "F")),
-              party=factor(c("A", "A", "B", "B", "B", "B", "A", "A", "A", "B", "B")))
-descr(dat, "gender", test_options = c(exact=TRUE))
-#> Warning in stats::prop.test(table(var, group), correct = FALSE): Chi-squared
-#> approximation may be incorrect
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-F -
-		 -
-M -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=5) - -(N=6) - -(N=11) - - -
-party -
-A - -3 (60%) - -2 (33%) - -5 (45%) - -0.491Bolo - -Prop. dif. CI -
-B - -2 (40%) - -4 (67%) - -6 (55%) - - -[-0.3, 0.84] -
-Bolo Boschloo’s test -
-
-exact2x2::boschloo(3, 5, 2, 6, tsmethod="central")
-#> 
-#>  Boschloo's test
-#> 
-#> data:  x1/n1=(3/5) and x2/n2= (2/6)
-#> proportion 1 = 0.6, proportion 2 = 0.33333, p-value = 0.4909
-#> alternative hypothesis: true p2(1-p1)/[p1(1-p2)] is less than 1
-#>  percent confidence interval:
-#>  NA NA
-#> sample estimates:
-#> p2(1-p1)/[p1(1-p2)] 
-#>           0.3333333
-

However, we can compare the exact2x2::boschloo with tsmethod=minlike to Exact::exact.test:

-
-exact2x2::boschloo(3, 5, 2, 6, tsmethod="minlike")
-#> 
-#>  Boschloo's test
-#> 
-#> data:  x1/n1=(3/5) and x2/n2= (2/6)
-#> proportion 1 = 0.6, proportion 2 = 0.33333, p-value = 0.5488
-#> alternative hypothesis: true p2(1-p1)/[p1(1-p2)] is not equal to 1
-#> 95 percent confidence interval:
-#>  NA NA
-#> sample estimates:
-#> p2(1-p1)/[p1(1-p2)] 
-#>           0.3333333
-Exact::exact.test(table(dat), method="boschloo", to.plot = FALSE)
-#> 
-#>  Boschloo's Exact Test
-#> 
-#> data:  3 out of 5 vs. 2 out of 6
-#> test statistic = 0.5671, first sample size = 5, second sample size = 6,
-#> p-value = 0.5488
-#> alternative hypothesis: true difference in proportion is not equal to 0
-#> sample estimates:
-#> difference in proportion 
-#>                0.2666667
-
-
- - - -
- - - -
-

Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

-
- -
-

Site built with pkgdown 1.6.1.

-
- -
-
- - - - - - diff --git a/docs/articles/other_software_comparison_files/header-attrs-2.6/header-attrs.js b/docs/articles/other_software_comparison_files/header-attrs-2.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/other_software_comparison_files/header-attrs-2.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. 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We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/other_software_comparison_files/header-attrs-2.8.6/header-attrs.js b/docs/articles/other_software_comparison_files/header-attrs-2.8.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/other_software_comparison_files/header-attrs-2.8.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. 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zEU{sN!Dv6ApkpVfUQ`=|u@EX2!&qQICLS=BjEcoDcyLAGfm02b?FZvXcxFEUVHeqU zdX*k99!B{t41yiAU0^)AkB1>VYP{^kS+XH+muK^^<^-T45MgCNV+x?E$Azl z+c``kqU)E09*@IhESfKHA?A1mMl`5C6ebf - - - - - - -Usage guidance • DescrTab2 - - - - - - - - - - - -
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Usage guidance

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2021-10-08

- - Source: vignettes/usage_guide.Rmd - - -
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-Introduction

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DescrTab2 is the replacement of the DescrTab package. It supports a variety of different customization options and can be used in .Rmd files in conjunction with knitr.

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-Preamble settings

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DescrTab2 works in your R-console, as well as in .Rmd documents corresponding to output formats of the type pdf_documument, html_document and word_document. It even supports YAML-headers with multiple output formats! For example, if your YAML-header looks like the example below, DescrTab2 should automagically detect the output format depending on the rendering option you choose from the dropdown menue (the arrow next to the “Knit” button on the top menue bar).

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---
-title: "DescrTab2 tutorial"
-output:
-  word_document: default
-  pdf_document: default
-  html_document: default
----
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Required LaTeX packages should be loaded automatically as well when rendering as a pdf.

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-Getting started

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Make sure you include the DescrTab2 library by typing

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somewhere in the document before you use it. You are now ready to go!

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For instructive purposes, we will use the following dataset:

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-dat <- iris[, c("Species", "Sepal.Length")]
-dat %<>% mutate(animal= c("Mammal", "Fish") %>% rep(75) %>% factor())
-dat %<>% mutate(food= c("fries", "wedges") %>% sample(150, TRUE) %>% factor())
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Producing beautiful descriptive tables is now as easy as typing:

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-Variables -
-		 -
-Total -
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-p -
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- -(N=150) - -
-Species -
-setosa - -50 (33%) - ->0.999chi1 -
-versicolor - -50 (33%) - -
-virginica - -50 (33%) - -
-Sepal.Length -
-N - -150 - -<0.001tt1 -
-mean - -5.8 - -
-sd - -0.83 - -
-median - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -
-animal -
-Fish - -75 (50%) - ->0.999chi1 -
-Mammal - -75 (50%) - -
-food -
-fries - -82 (55%) - -0.253chi1 -
-wedges - -68 (45%) - -
-chi1 Chi-squared goodness-of-fit test -
-tt1 Student’s one-sample t-test -
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-Accessing table elements

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The object returned from the descr function is basically just a named list. You may be interested in referencing certain summary statistics from the table in your document. To do this, you can save the list returned by descr:

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-my_table <- descr(dat)
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You can then access the elements of the list using the $ operator.

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-my_table$variables$Sepal.Length$results$Total$mean
-#> [1] 5.843333
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Rstudios autocomplete suggestions are very helpful when navigating this list.

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The print function returns a formatted version of this list, which you can also save and access using the same syntax.

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-my_table <- descr(dat) %>% print(silent=TRUE)
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-Specifying a group

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Use the group option to specify the name of a grouping variable in your data:

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-descr(dat, "Species")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-Variables -
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-setosa -
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-versicolor -
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-virginica -
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-Total -
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-p -
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- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
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-Assigning labels

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Use the group_labels option to assign group labels and the var_labels option to assign variable labels:

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-descr(dat, "Species", group_labels=list(setosa="My custom group label"), var_labels = list(Sepal.Length = "My custom variable label"))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-Variables -
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-My custom group label -
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-versicolor -
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-virginica -
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-Total -
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-p -
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- -(N=50) - -(N=50) - -(N=150) - - -
-My custom variable label -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
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-Assigning a table caption

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Use the caption member of the format_options argument to assign a table caption:

-
-descr(dat, "Species", format_options = list(caption="Description of our example dataset."))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-Description of our example dataset. -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
-
-
-

-Confidence intervals for two group comparisons

-

For 2-group comparisons, decrtab automatically calculates confidence intervals for differences in effect measures:

-
-descr(dat, "animal")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - ->0.999chi2 - -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.961tt2 - -Mean dif. CI -
-mean - -5.8 - -5.8 - -5.8 - - -[-0.26, 0.27] -
-sd - -0.86 - -0.81 - -0.83 - - -
-median - -5.7 - -5.8 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - - -
-food -
-fries - -39 (52%) - -43 (57%) - -82 (55%) - -0.512chi2 - -Prop. dif. CI -
-wedges - -36 (48%) - -32 (43%) - -68 (45%) - - -[-0.21, 0.11] -
-chi2 Pearson’s chi-squared test -
-tt2 Welch’s two-sample t-test -
-
-
-

-Different tests

-

There are a lot of different tests available. Check out the test_choice vignette for details: https://imbi-heidelberg.github.io/DescrTab2/articles/test_choice_tree_pdf.pdf

-

Here are some different tests in action:

-
-descr(dat %>% select(-"Species"), "animal", test_options = list(exact=TRUE, nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.870MWU - -HL CI -
-mean - -5.8 - -5.8 - -5.8 - - -[-0.3, 0.3] -
-sd - -0.86 - -0.81 - -0.83 - - -
-median - -5.7 - -5.8 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - - -
-food -
-fries - -39 (52%) - -43 (57%) - -82 (55%) - -0.554Bolo - -Prop. dif. CI -
-wedges - -36 (48%) - -32 (43%) - -68 (45%) - - -[-0.21, 0.11] -
-MWU Mann-Whitney’s U test -
-Bolo Boschloo’s test -
-
-descr(dat %>% select(c("Species", "Sepal.Length")), "Species", test_options = list(nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001KW -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-KW Kruskal-Wallis’s one-way ANOVA -
-
-
-

-Paired observations

-

In situations with paired data, the group variable usually denotes the timing of the measurement (e.g. “before” and “after” or “time 1”, “time 2”, etc.). In these scenarios, you need an additional index variable that specifies which observations from the different timepoints should be paired. The test_options =list(paired=TRUE, indices = <Character name of index variable name or vector of indices>) option can be used to specify the pairing indices, see the example below. DescrTab2 only works with data in “long format”, see e.g. ?reshape or ?tidyr::pivot_longer for information on how to transoform your data from wide to long format.

-
-descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal")) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices=rep(1:75, each=2)))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
-#> Warning in test_cat(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
-#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
-#> exact McNemar's test.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Before -
-		 -
-After -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - - -
-Sepal.Length -
-N - -75 - -75 - -0.937tpar - -Mean dif. CI -
-mean - -5.8 - -5.8 - - -[-0.16, 0.18] -
-sd - -0.86 - -0.81 - - -
-median - -5.7 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - - -
-food -
-fries - -39 (52%) - -43 (57%) - -0.635McN - -Prop. dif. CI -
-wedges - -36 (48%) - -32 (43%) - - -[-0.21, 0.11] -
-tpar Student’s paired t-test -
-McN McNemar’s test -
-
-descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal"), idx = rep(1:75, each=2)) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices="idx" ))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
-#> Warning in test_cat(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
-#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
-#> exact McNemar's test.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Before -
-		 -
-After -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - - -
-Sepal.Length -
-N - -75 - -75 - -0.937tpar - -Mean dif. CI -
-mean - -5.8 - -5.8 - - -[-0.16, 0.18] -
-sd - -0.86 - -0.81 - - -
-median - -5.7 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - - -
-food -
-fries - -39 (52%) - -43 (57%) - -0.635McN - -Prop. dif. CI -
-wedges - -36 (48%) - -32 (43%) - - -[-0.21, 0.11] -
-tpar Student’s paired t-test -
-McN McNemar’s test -
-
-
-

-Significant digits

-

Every summary statistic in DescrTab2 is formatted by a corresponding formatting function. You can exchange these formatting functions as you please:

-
-descr(dat, "Species", format_summary_stats = list(mean=function(x)formatC(x, digits = 4)) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -5.006 - -5.936 - -6.588 - -5.843 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
-
-
-

-Omitting summary statistics

-

Let’s say you don’t want to calculate quantiles for your numeric variables. You can specify the summary_stats_cont option to include all summary statistics but quantiles:

-
-descr(dat, "Species", summary_stats_cont = list(N = DescrTab2:::.N, Nmiss = DescrTab2:::.Nmiss, mean =
-    DescrTab2:::.mean, sd = DescrTab2:::.sd, median = DescrTab2:::.median, min = DescrTab2:::.min, max =
-    DescrTab2:::.max))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
-
-
-

-Adding summary statistics

-

Let’s say you have a categorical variable, but for some reason it’s levels are numerals and you want to calculate the mean. No problem:

-
-# Create example dataset
-dat2 <- iris
-dat2$cat_var <- c(1,2) %>% sample(150, TRUE) %>% factor()
-dat2 <- dat2[, c("Species", "cat_var")]
-
-descr(dat2, "Species", summary_stats_cat=list(mean=DescrTab2:::.factormean))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-cat_var -
-mean - -1.5 - -1.6 - -1.4 - -1.5 - -0.132chi2 -
-1 - -24 (48%) - -20 (40%) - -30 (60%) - -74 (49%) - -
-2 - -26 (52%) - -30 (60%) - -20 (40%) - -76 (51%) - -
-chi2 Pearson’s chi-squared test -
-
-
-

-Combining mean and sd

-

Use the format_options = list(combine_mean_sd=TRUE) option:

-
-descr(dat, "Species", format_options = c(combine_mean_sd=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean ± sd - -5 ± 0.35 - -5.9 ± 0.52 - -6.6 ± 0.64 - -5.8 ± 0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999chi2 -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -27 (54%) - -26 (52%) - -29 (58%) - -82 (55%) - -0.828chi2 -
-wedges - -23 (46%) - -24 (48%) - -21 (42%) - -68 (45%) - -
-F F-test (ANOVA) -
-chi2 Pearson’s chi-squared test -
-
-
-

-Omitting p-values

-

You can declare the format_options = list(print_p = FALSE) option to omit p-values:

-
-descr(dat, "animal", format_options = list(print_p = FALSE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - -
-Sepal.Length -
-N - -75 - -75 - -150 - -Mean dif. CI -
-mean - -5.8 - -5.8 - -5.8 - -[-0.26, 0.27] -
-sd - -0.86 - -0.81 - -0.83 - -
-median - -5.7 - -5.8 - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - -
-food -
-fries - -39 (52%) - -43 (57%) - -82 (55%) - -Prop. dif. CI -
-wedges - -36 (48%) - -32 (43%) - -68 (45%) - -[-0.21, 0.11] -
-

Similarily for Confidence intervals:

-
-descr(dat, "animal", format_options = list(print_CI = FALSE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-
- -(N=75) - -(N=75) - -(N=150) - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - ->0.999chi2 -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.961tt2 -
-mean - -5.8 - -5.8 - -5.8 - -
-sd - -0.86 - -0.81 - -0.83 - -
-median - -5.7 - -5.8 - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - -
-food -
-fries - -39 (52%) - -43 (57%) - -82 (55%) - -0.512chi2 -
-wedges - -36 (48%) - -32 (43%) - -68 (45%) - -
-chi2 Pearson’s chi-squared test -
-tt2 Welch’s two-sample t-test -
-
-
-

-Controling options on a per-variable level

-

You can use the var_options list to control formatting and test options on a per-variable basis. Let’s say in the dataset iris, we want that only the Sepal.Length variable has more digits in the mean and a nonparametric test:

-
-descr(iris, "Species", var_options = list(Sepal.Length = list(
-  format_summary_stats = list(
-    mean = function(x)
-      formatC(x, digits = 4)
-  ),
-  test_options = c(nonparametric = TRUE)
-)))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001KW -
-mean - -5.006 - -5.936 - -6.588 - -5.843 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-Sepal.Width -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -3.4 - -2.8 - -3 - -3.1 - -
-sd - -0.38 - -0.31 - -0.32 - -0.44 - -
-median - -3.4 - -2.8 - -3 - -3 - -
-Q1 - Q3 - -3.2 – 3.7 - -2.5 – 3 - -2.8 – 3.2 - -2.8 – 3.3 - -
-min - max - -2.3 – 4.4 - -2 – 3.4 - -2.2 – 3.8 - -2 – 4.4 - -
-Petal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -1.5 - -4.3 - -5.6 - -3.8 - -
-sd - -0.17 - -0.47 - -0.55 - -1.8 - -
-median - -1.5 - -4.3 - -5.5 - -4.3 - -
-Q1 - Q3 - -1.4 – 1.6 - -4 – 4.6 - -5.1 – 5.9 - -1.6 – 5.1 - -
-min - max - -1 – 1.9 - -3 – 5.1 - -4.5 – 6.9 - -1 – 6.9 - -
-Petal.Width -
-N - -50 - -50 - -50 - -150 - -<0.001F -
-mean - -0.25 - -1.3 - -2 - -1.2 - -
-sd - -0.11 - -0.2 - -0.27 - -0.76 - -
-median - -0.2 - -1.3 - -2 - -1.3 - -
-Q1 - Q3 - -0.2 – 0.3 - -1.2 – 1.5 - -1.8 – 2.3 - -0.3 – 1.8 - -
-min - max - -0.1 – 0.6 - -1 – 1.8 - -1.4 – 2.5 - -0.1 – 2.5 - -
-KW Kruskal-Wallis’s one-way ANOVA -
-F F-test (ANOVA) -
-
-
- - - -
- - - -
-

Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

-
- -
-

Site built with pkgdown 1.6.1.

-
- -
-
- - - - - - diff --git a/docs/articles/usage_guide_files/header-attrs-2.6/header-attrs.js b/docs/articles/usage_guide_files/header-attrs-2.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/usage_guide_files/header-attrs-2.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/usage_guide_files/header-attrs-2.7/header-attrs.js b/docs/articles/usage_guide_files/header-attrs-2.7/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/usage_guide_files/header-attrs-2.7/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/usage_guide_files/header-attrs-2.8.6/header-attrs.js b/docs/articles/usage_guide_files/header-attrs-2.8.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/usage_guide_files/header-attrs-2.8.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/validation_statement.html b/docs/articles/validation_statement.html deleted file mode 100644 index 8d55bbd..0000000 --- a/docs/articles/validation_statement.html +++ /dev/null @@ -1,158 +0,0 @@ - - - - - - - -Validation statement • DescrTab2 - - - - - - - - - - - -
-
- - - - -
-
-
-

Validation statement

- - - Source: vignettes/validation_statement.Rmd - - -
- - - -
-

-Liability

-

As noted in the GPL-3 License, neither the authors nor any person or institution associated with the creation, production or distribution of DescrTab2 is liable of any damages caused by the use of the software:

-
-

IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

-
-
-
-

-Unit tests

-

DescrTab2 features 100% covr unit test coverage, which means that each line of code is executed at least once by some automated unit test. Most of these unit tests only check whether the code associated with it produces errors. This aims to ensure that the various options in DescrTab2 work error-free with a variations of possible input datasets and edge-cases.

-
-
-

-Manual external software comparisons

-

Comparisons of the results produced by DescrTab2 with other software are not automated. The user may feel free to perform such comparisons by themselves. An exemplary comparison can be examined in this document.

-
-
-

-Dependencies

-

DescrTab2 relies on various other packages to perform its designated purpose. Most of the imported packages are neccesary to facilitate tasks related to data-wrangling and output formatting, namely ?utils, ?dplyr, ?rlang, ?tibble, ?stringr, ?forcats, ?magrittr, ?tidyselect, ?scales, ?cli, ?kableExtra, ?flextable and ?officer. The pre-implemented summary statistics (mean, sd, etc.) make use of functions provided for this purpose in base R (see ?base and ?stats). The test functions used for the calculcation of p-values are mostly from base R (e.g. ?t.test from ?stats) and the recommended packages (e.g. ?lme from ?nlme for the Mixed Model Anova p-value). Notable exceptions are the Cochrane Q Test from the ?DescTools package (?DescTools::CochranQTest) and the Fisher-Boschloo test from the ?exact2x2 package (?exact2x2::boschloo).

-
-
- - - -
- - - -
-

Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

-
- -
-

Site built with pkgdown 1.6.1.

-
- -
-
- - - - - - diff --git a/docs/articles/validation_statement_files/header-attrs-2.6/header-attrs.js b/docs/articles/validation_statement_files/header-attrs-2.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/validation_statement_files/header-attrs-2.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/validation_statement_files/header-attrs-2.7/header-attrs.js b/docs/articles/validation_statement_files/header-attrs-2.7/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/validation_statement_files/header-attrs-2.7/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/validation_statement_files/header-attrs-2.8.6/header-attrs.js b/docs/articles/validation_statement_files/header-attrs-2.8.6/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/validation_statement_files/header-attrs-2.8.6/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 58329d2..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,148 +0,0 @@ - -Authors and Citation • DescrTab2 - - -
-
- - - -
-
-
-
-

Authors

-
- - -
  • -

    Jan Meis. Author, maintainer. -

    -
    • -
  • -

    Lukas Baumann. Author. -

    -
    • -
  • -

    Maximilian Pilz. Author. -

    -
    • -
  • -

    Lukas Sauer. Author. -

    -
    • -
  • -

    Lorenz Uhlmann. Contributor. -

    -
    • -
  • -

    Csilla van Lunteren. Contributor. -

    -
    • -
  • -

    Kevin Kunzmann. Contributor. -

    -
    • -
  • -

    Hao Zhu. Contributor. -

    -
    • -
-
-
-

Citation

- Source: DESCRIPTION -
-
- - -

Meis J, Baumann L, Pilz M, Sauer L (2022). -DescrTab2: Publication Quality Descriptive Statistics Tables. -R package version 2.1.9, https://imbi-heidelberg.github.io/DescrTab2/. -

- 
@Manual{,
-  title = {DescrTab2: Publication Quality Descriptive Statistics Tables},
-  author = {Jan Meis and Lukas Baumann and Maximilian Pilz and Lukas Sauer},
-  year = {2022},
-  note = {R package version 2.1.9},
-  url = {https://imbi-heidelberg.github.io/DescrTab2/},
-}
- -
- -
- - - -
-

Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

-
- -
-

Site built with pkgdown 2.0.2.

-
- -
- - - - - - - - diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css deleted file mode 100644 index 5a85941..0000000 --- a/docs/bootstrap-toc.css +++ /dev/null @@ -1,60 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ - -/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ - -/* All levels of nav */ -nav[data-toggle='toc'] .nav > li > a { - display: block; - padding: 4px 20px; - font-size: 13px; - font-weight: 500; - color: #767676; -} -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 19px; - color: #563d7c; - text-decoration: none; - background-color: transparent; - border-left: 1px solid #563d7c; -} -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 18px; - font-weight: bold; - color: #563d7c; - background-color: transparent; - border-left: 2px solid #563d7c; -} - -/* Nav: second level (shown on .active) */ -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} -nav[data-toggle='toc'] .nav .nav > li > a { - padding-top: 1px; - padding-bottom: 1px; - padding-left: 30px; - font-size: 12px; - font-weight: normal; -} -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 29px; -} -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 28px; - font-weight: 500; -} - -/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ -nav[data-toggle='toc'] .nav > .active > ul { - display: block; -} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js deleted file mode 100644 index 1cdd573..0000000 --- a/docs/bootstrap-toc.js +++ /dev/null @@ -1,159 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $('
    '); - }, - - createChildNavList: function($parent) { - var $childList = this.createNavList(); - $parent.append($childList); - return $childList; - }, - - generateNavEl: function(anchor, text) { - var $a = $(''); - $a.attr('href', '#' + anchor); - $a.text(text); - var $li = $('
  • '); - $li.append($a); - return $li; - }, - - generateNavItem: function(headingEl) { - var anchor = this.generateAnchor(headingEl); - var $heading = $(headingEl); - var text = $heading.data('toc-text') || $heading.text(); - return this.generateNavEl(anchor, text); - }, - - // Find the first heading level (`

    `, then `

    `, etc.) that has more than one element. Defaults to 1 (for `

    `). - getTopLevel: function($scope) { - for (var i = 1; i <= 6; i++) { - var $headings = this.findOrFilter($scope, 'h' + i); - if ($headings.length > 1) { - return i; - } - } - - return 1; - }, - - // returns the elements for the top level, and the next below it - getHeadings: function($scope, topLevel) { - var topSelector = 'h' + topLevel; - - var secondaryLevel = topLevel + 1; - var secondarySelector = 'h' + secondaryLevel; - - return this.findOrFilter($scope, topSelector + ',' + secondarySelector); - }, - - getNavLevel: function(el) { - return parseInt(el.tagName.charAt(1), 10); - }, - - populateNav: function($topContext, topLevel, $headings) { - var $context = $topContext; - var $prevNav; - - var helpers = this; - $headings.each(function(i, el) { - var $newNav = helpers.generateNavItem(el); - var navLevel = helpers.getNavLevel(el); - - // determine the proper $context - if (navLevel === topLevel) { - // use top level - $context = $topContext; - } else if ($prevNav && $context === $topContext) { - // create a new level of the tree and switch to it - $context = helpers.createChildNavList($prevNav); - } // else use the current $context - - $context.append($newNav); - - $prevNav = $newNav; - }); - }, - - parseOps: function(arg) { - var opts; - if (arg.jquery) { - opts = { - $nav: arg - }; - } else { - opts = arg; - } - opts.$scope = opts.$scope || $(document.body); - return opts; - } - }, - - // accepts a jQuery object, or an options object - init: function(opts) { - opts = this.helpers.parseOps(opts); - - // ensure that the data attribute is in place for styling - opts.$nav.attr('data-toggle', 'toc'); - - var $topContext = this.helpers.createChildNavList(opts.$nav); - var topLevel = this.helpers.getTopLevel(opts.$scope); - var $headings = this.helpers.getHeadings(opts.$scope, topLevel); - this.helpers.populateNav($topContext, topLevel, $headings); - } - }; - - $(function() { - $('nav[data-toggle="toc"]').each(function(i, el) { - var $nav = $(el); - Toc.init($nav); - }); - }); -})(); diff --git a/docs/docsearch.css b/docs/docsearch.css deleted file mode 100644 index e5f1fe1..0000000 --- a/docs/docsearch.css +++ /dev/null @@ -1,148 +0,0 @@ -/* Docsearch -------------------------------------------------------------- */ -/* - Source: https://github.com/algolia/docsearch/ - License: MIT -*/ - -.algolia-autocomplete { - display: block; - -webkit-box-flex: 1; - -ms-flex: 1; - flex: 1 -} - -.algolia-autocomplete .ds-dropdown-menu { - width: 100%; - min-width: none; - max-width: none; - padding: .75rem 0; - background-color: #fff; - background-clip: padding-box; - border: 1px solid rgba(0, 0, 0, .1); - box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175); -} - -@media (min-width:768px) { - .algolia-autocomplete .ds-dropdown-menu { - width: 175% - } -} - -.algolia-autocomplete .ds-dropdown-menu::before { - display: none -} - -.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] { - padding: 0; - background-color: rgb(255,255,255); - border: 0; - max-height: 80vh; -} - -.algolia-autocomplete .ds-dropdown-menu .ds-suggestions { - margin-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion { - padding: 0; - overflow: visible -} - -.algolia-autocomplete .algolia-docsearch-suggestion--category-header { - padding: .125rem 1rem; - margin-top: 0; - font-size: 1.3em; - font-weight: 500; - color: #00008B; - border-bottom: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--wrapper { - float: none; - padding-top: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { - float: none; - width: auto; - padding: 0; - text-align: left -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content { - float: none; - width: auto; - padding: 0 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--content::before { - display: none -} - -.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { - padding-top: .75rem; - margin-top: .75rem; - border-top: 1px solid rgba(0, 0, 0, .1) -} - -.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { - display: block; - padding: .1rem 1rem; - margin-bottom: 0.1; - font-size: 1.0em; - font-weight: 400 - /* display: none */ -} - -.algolia-autocomplete .algolia-docsearch-suggestion--title { - display: block; - padding: .25rem 1rem; - margin-bottom: 0; - font-size: 0.9em; - font-weight: 400 -} - -.algolia-autocomplete .algolia-docsearch-suggestion--text { - padding: 0 1rem .5rem; - margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504c..0000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); - $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/extra.css b/docs/extra.css deleted file mode 100644 index bd7d93c..0000000 --- a/docs/extra.css +++ /dev/null @@ -1,3 +0,0 @@ -div.contents { - font-size: 14px; -} \ No newline at end of file diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index 18f601a..0000000 --- a/docs/index.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - -Publication Quality Descriptive Statistics Tables • DescrTab2 - - - - - - - - - - - - - -
    -
    - - - - -
    -
    - - -
    -

    DescrTab2 -

    -
    -

    Publication quality descriptive statistics tables with R -

    -

    Provides functions to create descriptive statistics tables for continuous and categorical variables. By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies for categorical variables are calculated. DescrTab2 features a sophisticated algorithm to choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group differences of appropriated summary measures are automatically produces for two-group comparison.

    -

    Tables generated by DescrTab2 can be integrated in a variety of document formats, including .html, .tex and .docx documents. DescrTab2 also allows printing tables to console and saving table objects for later use.

    -

    You can also install the development version of DescrTab2 (recommended) from github by typing:

    -
    -remotes::install_github("https://github.com/imbi-heidelberg/DescrTab2")
    -

    You may also install the stable version of DescrTab2 from cran by typing

    -
    -install.packages("DescrTab2")
    -

    into your R console.

    -

    This article may be useful to get aquainted with DescTab2:

    -

    https://imbi-heidelberg.github.io/DescrTab2/articles/a_usage_guide.html

    -

    You may also check out our documentation page which hosts serveral long-form documentation articles:

    -

    https://imbi-heidelberg.github.io/DescrTab2/

    -
    -
    - -
    - - -
    - - -
    -

    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    -

    Site built with pkgdown 2.0.2.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad827..0000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/news/index.html b/docs/news/index.html deleted file mode 100644 index eea2d5e..0000000 --- a/docs/news/index.html +++ /dev/null @@ -1,194 +0,0 @@ - -Changelog • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Changelog

    - Source: NEWS.md -
    - -
    -

    Version 2.1.9

    -
    • Fix a bug where ordered factors were not correctly converted to rank data for non-parametric testing purposes
      • -
    -
    -

    Version 2.1.8

    -
    • Add check in .meanCIlower and .meanCIupper for non-constantness of data
      • -
    -
    -

    Version 2.1.72021-12-02

    -
    • Fixed a bug where indices could not be specified inside var_options
      • -
    • fixed bug with printing CI abbreviations in tex mode
      • -
    -
    -

    Version 2.1.6

    -
    • Add hline between footnotes and table in tex mode
      • -
    • make pagebreak alogrithm less aggressive
      • -
    -
    -

    Version 2.1.5

    -
    • Fix bug with group_labels that lead to group N numbers not being dispalyed.
      • -
    -
    -

    Version 2.1.4

    -
    • Fix major bug where percentages were not calculated correctly if factor levels are omitted.
      • -
    • Fix bug with confidence interval functions
      • -
    -
    -

    Version 2.1.3

    -
    • Added lapply_descr function which allows applying descr to a list of datasets
      • -
    -
    -

    Version 2.1.2

    -
    • Major code refactoring, removing alot of code duplication
      • -
    • The confidence interval column now behaves more similarily to the p-value colum and now supports footnotes.
      • -
    • Fixed github actions and updated documentation
      • -
    • Added Fisher’s exact test for KxL tables
      • -
    • Added exact binomial test for 1xL tables
      • -
    -
    -

    Version 2.1.1

    -
    • Fixed some issue in the usage documentation where a code chuck was accidentally hidden
      • -
    • Some work on github actions
      • -
    -
    -

    Version 2.1

    -
    • knit_print now works properly. R chunks in .Rmd documents don’t need the results = ‘asis’ option anymore.
      • -
    • Tables can now have captions
      • -
    -
    -

    Version 2.0.13

    -
    • LaTeX tables will now properly wrap the names of long factor levels and long variable names.
      • -
    -
    -

    Version 2.0.12

    -
    • .mean now returns NA_real_ in cases were NaN was previously returned.
      • -
    -
    -

    Version 2.0.11

    -
    • Fixed a bug with paired data, where some of the pairs contain missing. In some cases, tests were not calculated and a misleading error message was produced.
      • -
    -
    -

    Version 2.0.10

    -
    • Added the ability to suppress the “Total” column via the format_options(print_Total = FALSE) option.
      • -
    • Exact McNemars test will now calculate confidence intervals for rate differences by leveraging the distribution of the test statistic.
      • -
    • Using (non-exact) McNemars test will now produce a warning, that the confidence intervals for the differences in rates do not consider the paired structure of the data.
      • -
    • Datasets containing variables which inherit from the “Date” class are now automatically converted to factors.
      • -
    -
    -

    Version 2.0.9

    -
    • Fixed a bug with print_CI = FALSE option in format_options.
      • -
    -
    -

    Version 2.0.8

    -
    • “&” signs in factor labels are now properly escaped in LaTeX code.
      • -
    -
    -

    Version 2.0.72021-03-15

    -
    • Added the “combine_median_Q1_Q3” argument to format_options, which reshapes these summary statistics to “median (Q1, Q3)”.
      • -
    -
    -

    Version 2.0.6

    -
    • Added the following members to format_options: percent_suffix, row_percent, Nmiss_row_percent and absolute_relative_frequency_mode. They can be used to control control how absolute and relative frequencies are displayed.
      • -
    -
    -

    Version 2.0.5

    -
    • Added documentation for confidence intervals.
      • -
    -
    -

    Version 2.0.4

    -
    • All tests that previously used continuity correction by default do not use continuity correction anymore.
      • -
    • Added the additional_test_args argument to test_options. This lets the user pass arguments to the underlying test functions, e.g. additional_test_args = list(correct=TRUE) to request continuity correction in chisq.test.
      • -
    -
    -

    Version 2.0.32020-12-16

    -
    • First cran release.
      • -
    -
    - - - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index 80ea5b8..0000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,384 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; 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-} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} - -/* Section anchors --------------------------------- - Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71 -*/ - -div.csl-bib-body { } -div.csl-entry { - clear: both; 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- - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $("div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index e34e279..0000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,14 +0,0 @@ -pandoc: 2.16.1 -pkgdown: 2.0.2 -pkgdown_sha: ~ -articles: - a_usage_guide: a_usage_guide.html - b_test_choice_tree_pdf: b_test_choice_tree_pdf.pdf - c_other_software_comparison: c_other_software_comparison.html - d_validation_statement: d_validation_statement.html - e_maintenance_guide: e_maintenance_guide.html -last_built: 2022-01-20T13:29Z -urls: - reference: https://imbi-heidelberg.github.io/DescrTab2/reference - article: https://imbi-heidelberg.github.io/DescrTab2/articles - diff --git a/docs/reference/DescrTab2.html b/docs/reference/DescrTab2.html deleted file mode 100644 index 40c4287..0000000 --- a/docs/reference/DescrTab2.html +++ /dev/null @@ -1,115 +0,0 @@ - -DescrTab2 — DescrTab2 • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    DescrTab2

    - Source: R/DescrTab2.R - -
    - -
    -

    Publication quality descriptive statistics tables with R

    -
    - - -
    -

    Details

    -

    Provides functions to create descriptive statistics tables for continuous and categorical variables. -By default, summary statistics such as mean, standard deviation, quantiles, minimum and maximum for continuous -variables and relative and absolute frequencies for categorical variables are calculated. 'DescrTab2' features a sophisticated algorithm to -choose appropriate test statistics for your data and provides p-values. On top of this, confidence intervals for group -differences of appropriated summary measures are automatically produces for two-group comparison. -Tables generated by 'DescrTab2' can be integrated in a variety of document formats, including .html, .tex and .docx documents. -'DescrTab2' also allows printing tables to console and saving table objects for later use.

    -

    Check out our documentation online: https://imbi-heidelberg.github.io/DescrTab2/ -or browse the help files in the Rstudio viewer. You can access the vignettes by typing: browseVignettes("DescrTab2")

    -

    The most important function you probably want to check out is called descr.

    -
    - -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png deleted file mode 100644 index 17a358060aed2a86950757bbd25c6f92c08c458f..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1011 zcmeAS@N?(olHy`uVBq!ia0y~yV0-|=9Be?5+AI5}0x7m6Z+90U4Fo@(ch>_c&H|6f zVg?3oArNM~bhqvg0|WD9PZ!6KiaBo&GBN^{G%5UFpXcEKVvd5*5Eu=C0SJK)8A6*F U7`aXvEC5;V>FVdQ&MBb@00SN#Z2$lO diff --git a/docs/reference/descr.html b/docs/reference/descr.html deleted file mode 100644 index 4c599f4..0000000 --- a/docs/reference/descr.html +++ /dev/null @@ -1,418 +0,0 @@ - -Calculate descriptive statistics — descr • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Calculate descriptive statistics

    - Source: R/descr.R - -
    - -
    -

    Generate a list of descriptive statistics. By default, the function calculates summary statistics such as mean, -standard deviation, quantiles, minimum and maximum for continuous variables and relative and absolute frequencies -for categorical variables. Also calculates p-values for an appropriately chosen statistical test. -For two-group comparisons, confidence intervals for appropriate summary measures of group differences are calculated aswell. In particular, -Wald confidence intervals from prop.test are used for categorical variables with 2 levels, confidence intervals from t.test -are used for continuous variables and confidence intervals for the Hodges-Lehman estimator [1] from wilcox.test are used for ordinal variables.

    -
    - -
    - 
    descr(
-  dat,
-  group = NULL,
-  group_labels = list(),
-  var_labels = list(),
-  var_options = list(),
-  summary_stats_cont = list(N = DescrTab2:::.N, Nmiss = DescrTab2:::.Nmiss, mean =
-    DescrTab2:::.mean, sd = DescrTab2:::.sd, median = DescrTab2:::.median, Q1 =
-    DescrTab2:::.Q1, Q3 = DescrTab2:::.Q3, min = DescrTab2:::.min, max =
-    DescrTab2:::.max),
-  summary_stats_cat = list(),
-  format_summary_stats = list(N = function(x) {     format(x, digits = 2, scientific =
-    3) }, mean = function(x) {     format(x, digits = 2, scientific = 3) }, sd =
-    function(x) {     format(x, digits = 2, scientific = 3) }, median = function(x) {    
-    format(x, digits = 2, scientific = 3) }, Q1 = function(x) {     format(x, digits = 2,
-    scientific = 3) }, Q3 = function(x) {     format(x, digits = 2, scientific = 3) },
-    min = function(x) {     format(x, digits = 2, scientific = 3) }, max = function(x) { 
-       format(x, digits = 2, scientific = 3) }, CI = function(x) {     format(x, digits =
-    2, scientific = 3) }),
-  format_p = scales::pvalue_format(),
-  format_options = list(print_Total = NULL, print_p = TRUE, print_CI = TRUE,
-    combine_mean_sd = FALSE, combine_median_Q1_Q3 = FALSE, omit_factor_level = "none",
-    omit_Nmiss_if_0 = TRUE, omit_missings_in_group = TRUE, percent_accuracy = NULL,
-    percent_suffix = "%", row_percent = FALSE, Nmiss_row_percent = FALSE,
-    absolute_relative_frequency_mode = c("both", "only_absolute", "only_relative"),
-    omit_missings_in_categorical_var = FALSE, categorical_missing_percent_mode =
-    c("no_missing_percent", "missing_as_regular_category",     
-    "missing_as_separate_category"), caption = NULL, replace_empty_string_with_NA = TRUE,
-    categories_first_summary_stats_second = FALSE),
-  test_options = list(paired = FALSE, nonparametric = FALSE, exact = FALSE, indices =
-    c(), guess_id = FALSE, include_group_missings_in_test = FALSE,
-    include_categorical_missings_in_test = FALSE, test_override = NULL,
-    additional_test_args = list(), boschloo_max_n = 200),
-  reshape_rows = list(`Q1 - Q3` = list(args = c("Q1", "Q3"), fun = function(Q1, Q3) {  
-      paste0(Q1, " -- ", Q3) }), `min - max` = list(args = c("min", "max"), fun =
-    function(min, max) {     paste0(min, " -- ", max) })),
-  ...
-)
    -
    - -
    -

    Arguments

    -
    dat
    -

    Data frame or tibble. The data set to be analyzed. Can contain continuous or factor (also ordered) variables.

    -
    group
    -

    name (as character) of the group variable in dat.

    -
    group_labels
    -

    named list of labels for the levels of the group variable in dat.

    -
    var_labels
    -

    named list of variable labels.

    -
    var_options
    -

    named list of lists. For each variable, you can have special options that apply only to that variable. -These options are specified in this argument. See the details and examples for more explanation.

    -
    summary_stats_cont
    -

    named list of summary statistic functions to be used for numeric variables.

    -
    summary_stats_cat
    -

    named list of summary statistic function to be used for categorical variables.

    -
    format_summary_stats
    -

    named list of formatting functions for summary statistics.

    -
    format_p
    -

    formatting function for p-values.

    -
    format_options
    -

    named list of formatting options.

    -
    test_options
    -

    named list of test options.

    -
    reshape_rows
    -

    named list of lists. Describes how to combine different summary statistics into the same row.

    -
    ...
    -

    further argument to be passed along

    -
    -
    -

    Value

    -

    Returns a A DescrList object, which is a named list of descriptive statistics -which can be passed along to the print function to create -pretty summary tables.

    -
    -
    -

    Labels

    - - -

    group_labels and var_labels need to be named lists of character elements. The names of the list elements have to match the variable -names in your dataset. The values of the list elements are the labels that will be assigned to these variables when printing.

    -
    -
    -

    Custom summary statistics

    - - -

    summary_stats_cont and summary_stats_cat are both named lists of functions. The names of the list elements are -what will be displayed in the leftmost column of the descriptive table. These functions should take a vector and return -a value.
    -Each summary statistic has to have an associated formatting function in the format_summary_stats list. -The functions in format_summary_stats take a numeric value and convert it to a character string, e.g. 0.2531235 -> "0.2".
    -The format_p function converts p-values to character strings, e.g. 0.05 -> "0.05" or 0.000001 -> "<0.001".

    -
    -
    -

    Formatting options

    - - -

    Further formatting options can be specified in the format_options list. It contains the following members:

    • print_Total (logical) controls whether to print the "Total" column. If print_Total = NULL, print_Total will be set -to TRUE if test_options$paired == FALSE, else it will be set to FALSE.

      • -

    • print_p (logical) controls whether to print the p-value column.

      • -

    • print_CI (logical) controls whether to print the confidence intervals for group-differences.

      • -

    • combine_mean_sd (logical) controls whether to combine the mean and sd row into one mean ± sd row. This is a -shortcut argument for the specification of an appropriate entry in the reshape_rows argument.

      • -

    • combine_median_Q1_Q3 (logical) controls whether to combine the median, Q1 and Q3 row into one median (Q1, Q3) row. This is a -shortcut argument for the specification of an appropriate entry in the reshape_rows argument.

      • -

    • omit_Nmiss_if_0 (logical) controls whether to omit the Nmiss row in continuous variables there are no missings in the variable.

      • -

    • omit_missings_in_group (logical) controls whether to omit all observations where the group variable is missing.

      • -

    • percent_accuracy (numeric) A number to round to. Use (e.g.) 0.01 to show 2 decimal places of precision. If NULL, the default, uses a heuristic that -should ensure breaks have the minimum number of digits needed to show the difference between adjacent values. See documentation of scales::label_percent

      • -

    • percent_suffix (character) the symbol to be used where "%" is appropriate, sensible choices are usually "%" (default) or "" (i.e., empty string)

      • -

    • row_percent (logical) controls wheter percentages of regular categorical variables should be calculated column-wise (default) or row-wise

      • -

    • Nmiss_row_percent (logical) controls whether percentages of the "Nmiss"-statistic (number of missing values) should be calculated column-wise (default) or row-wise

      • -

    • absolute_relative_frequency_mode (character) controls how to display frequencies. -It may be set to one of the following options: -

      • "both" will display absolute and relative frequencies.

        • -

      • "only_absolute" will only display absolute frequencies.

        • -

      • "only_relative" will only display relative frequencies.

        • -
      • -

    • omit_missings_in_categorical_var (logical) controls whether to omit missing values in categorical variables completely.

      • -

    • categorical_missing_percent_mode (character) controls how to display percentages in categorical variables with a (Missing) category. -It may be set to one of the following options: -

      • "no_missing_percent" omits a percentage in the missing category entirely.

        • -

      • "missing_as_regular_category" treats (Missing) as a regular category for %-calculation -This means that if You have three categories: "A" with 10 counts, "B" with 10 counts and "(Missing)" with 10 counts, -they will become "A": 10 (33%), "B": 10 (33%), "(Missing)": 10 (33% purposes.)

        • -

      • "missing_as_separat_category" calculates (Missing) percentages with respect to -all observations (i.e. #(Missing) / N), but calculates all other catetgory percentages with respect to the non-missing -observations (e.g. #A / N_nonmissing). This means that if You have three categories: "A" with 10 counts, "B" with 10 counts -and "(Missing)" with 10 counts, they will become "A": 10 (50%), "B": 10 (50%), "(Missing)": 10 (33%)

        • -

      • "caption" adds a table caption to the LaTeX, Word or PDf document

        • -
      • -

    • replace_empty_string_with_NA (logical) controls whether empty strings ("") should be replaced -with missing value (NA_character_).

      • -

    • categories_first_summary_stats_second (logical) controls whether the categories should be printed first in the summary statistics table.

      • -
    -
    -

    Test options

    - - -

    test_options is a named list with test options. It's members paired, nonparametric, and -exact (logicals) control which test in the corresponding situation. For details, check out the vignette: -https://imbi-heidelberg.github.io/DescrTab2/articles/b_test_choice_tree_pdf.pdf. The test_options = list(test_override="<some test name>") option can be specified to force usage of a -specific test. This will produce errors if the data does not allow calculation of that specific test, so be wary. -Use print_test_names() to see a list of all available test names. If paired = TRUE is specified, you need to supply an index variable -indices that specifies which datapoints in your dataset are paired. indices may either be a length one character vector that describes -the name of the index variable in your dataset, or a vector containing the respective indices. -If you have guess_id set to TRUE (the default), DescrTab2 will try to guess -the ID variable from your dataset and report a warning if it succeedes. -See https://imbi-heidelberg.github.io/DescrTab2/articles/a_usage_guide.html#Paired-observations-1 -for a bit more explanation. The optional list additional_test_args can be used to pass arguments along to test functions, -e.g. additional_test_args=list(correct=TRUE) will request continuity correction if available.

    -
    -
    -

    Customization for single variables

    - - -

    The var_options list can be used to conduct customizations that should only apply to a single variable and leave -the rest of the table unchanged.
    var_options is a list of named lists. This means that each member of var_options is itself a list again. -The names of the list elements of var_options determine the variables to which the options will apply. -Let's say you have an age variable in your dataset. To change 'descr' options only for age, you will need to pass -a list of the form var_options = list(age = list(<Your options here>)).
    -You can replace <Your options here> with the following options:

    • label a character string containing the label for the variable

      • -

    • summary_stats a list of summary statistics. See section "Custom summary statistics"

      • -

    • format_summary_stats a list of formatting functions for summary statistics. See section "Custom summary statistics"

      • -

    • format_p a function to format p-values. See section "Custom summary statistics"

      • -

    • format_options a list of formatting options. See section "Formatting options"

      • -

    • test_options a list of test options. See section "Test options"

      • -

    • test_override manually specify the name of the test you want to apply. You can see a list of choices -by typing print_test_names(). Possible choices are: -

      • "Cochran's Q test"

        • -

      • "McNemar's test"

        • -

      • "Chi-squared goodness-of-fit test"

        • -

      • "Pearson's chi-squared test"

        • -

      • "Exact McNemar's test"

        • -

      • "Boschloo's test"

        • -

      • "Wilcoxon's one-sample signed-rank test"

        • -

      • "Mann-Whitney's U test"

        • -

      • "Kruskal-Wallis's one-way ANOVA"

        • -

      • "Student's paired t-test"

        • -

      • "Mixed model ANOVA"

        • -

      • "Student's one-sample t-test"

        • -

      • "Welch's two-sample t-test"

        • -

      • "F-test (ANOVA)"

        • -
      • -
    -
    -

    Combining rows

    - - -

    The reshape_rows argument offers a framework for combining multiple rows of the output table into a single one. -reshape_rows is a named list of lists. The names of it's member-lists determine the name that will be displayed -as the name of the combined summary stats in the table (e.g. "mean ± sd "). The member lists need to contain two -elements: args, contains the names of the summary statistics to be combined as characters, and -fun which contains a function to combine these summary stats. The argument names of this function need to match -the character strings specified in args. Check out the default options for an exemplary definition.

    -
    -
    -

    References

    -

    [1] Hodges, J. L.; Lehmann, E. L. (1963). "Estimation of location based on ranks". Annals of Mathematical Statistics. 34 (2): 598-611. doi:10.1214/aoms/1177704172. JSTOR 2238406. MR 0152070. Zbl 0203.21105. PE euclid.aoms/1177704172

    -
    - -
    -

    Examples

    - 
    descr(iris)
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-DescrList <- descr(iris)
-DescrList$variables$results$Sepal.Length$Total$mean
-#> NULL
-print(DescrList)
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-descr(iris, "Species")
-#>    Variables      setosa       versicolor   virginica    Total   p      Test    
-#>  1  Sepal.Length                                                                
-#>  2    N            50           50           50           150     <0.0~  F-test~
-#>  3    mean         5            5.9          6.6          5.8                   
-#>  4    sd           0.35         0.52         0.64         0.83                  
-#>  5    median       5            5.9          6.5          5.8                   
-#>  6    Q1 - Q3      4.8 -- 5.2   5.6 -- 6.3   6.2 -- 6.9   5.1 -~                
-#>  7    min - max    4.3 -- 5.8   4.9 -- 7     4.9 -- 7.9   4.3 -~                
-#>  8  Sepal.Width                                                                 
-#>  9    N            50           50           50           150     <0.0~  F-test~
-#> 10    mean         3.4          2.8          3            3.1                   
-#> 11    sd           0.38         0.31         0.32         0.44                  
-#> 12    median       3.4          2.8          3            3                     
-#> 13    Q1 - Q3      3.2 -- 3.7   2.5 -- 3     2.8 -- 3.2   2.8 -~                
-#> 14    min - max    2.3 -- 4.4   2 -- 3.4     2.2 -- 3.8   2 -- ~                
-#> 15  Petal.Length                                                                
-#> 16    N            50           50           50           150     <0.0~  F-test~
-#> 17    mean         1.5          4.3          5.6          3.8                   
-#> 18    sd           0.17         0.47         0.55         1.8                   
-#> 19    median       1.5          4.3          5.5          4.3                   
-#> 20    Q1 - Q3      1.4 -- 1.6   4 -- 4.6     5.1 -- 5.9   1.6 -~                
-#> 21    min - max    1 -- 1.9     3 -- 5.1     4.5 -- 6.9   1 -- ~                
-#> 22  Petal.Width                                                                 
-#> 23    N            50           50           50           150     <0.0~  F-test~
-#> 24    mean         0.25         1.3          2            1.2                   
-#> 25    sd           0.11         0.2          0.27         0.76                  
-#> 26    median       0.2          1.3          2            1.3                   
-#> 27    Q1 - Q3      0.2 -- 0.3   1.2 -- 1.5   1.8 -- 2.3   0.3 -~                
-#> 28    min - max    0.1 -- 0.6   1 -- 1.8     1.4 -- 2.5   0.1 -~                
-
    -
    -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/dot-onLoad.html b/docs/reference/dot-onLoad.html deleted file mode 100644 index ac9fe6e..0000000 --- a/docs/reference/dot-onLoad.html +++ /dev/null @@ -1,120 +0,0 @@ - -Load LaTeX packages — .onLoad • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Load LaTeX packages

    - Source: R/DescrTab2.R - -
    - -
    -

    Load LaTeX packages

    -
    - -
    - 
    .onLoad(libname = find.package("kableExtra"), pkgname = "kableExtra")
    -
    - -
    -

    Arguments

    -
    libname
    -

    library name

    -
    pkgname
    -

    package name

    -
    -
    -

    Details

    -

    Thanks to Hao Zhu and his package kableExtra.

    -
    -
    -

    Author

    -

    Hao Zhu

    -
    - -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/farrington.manning.html b/docs/reference/farrington.manning.html deleted file mode 100644 index 140f25e..0000000 --- a/docs/reference/farrington.manning.html +++ /dev/null @@ -1,192 +0,0 @@ - -Farrington-Manning test for rate difference — farrington.manning • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Farrington-Manning test for rate difference

    - Source: R/farringtonManning.R - -
    - -
    -

    The Farrington-Manning test for rate differences can be used to - compare the rate difference of successes between two groups to a preset value. - It uses an explicit formula for the standard deviation of the test statistic under - the null hypothesis [1].

    -
    - -
    - 
    farrington.manning(
-  group1,
-  group2,
-  delta = 0,
-  alternative = "greater",
-  alpha = 0.025
-)
    -
    - -
    -

    Arguments

    -
    group1
    -

    a logical vector of data from group 1, where TRUE indicates a success

    -
    group2
    -

    a logical vector of data from group 2, where TRUE indicates a success

    -
    delta
    -

    the rate difference under the null hypothesis

    -
    alternative
    -

    character string indicating the alternative to use, either of -"two.sided", "less", "greater"

    -
    alpha
    -

    the significance level (acceptable error of the first kind), -a two-sided confidence intnerval is returned with confidence level 1 - 2*alpha, such that -the lower bound is a valid one sided confidence interval at the confidence level 1 - alpha.

    -
    -
    -

    Value

    -

    A list of class "htest" containing the following components:

    statistic:the value of the Z-statistic
    parameter:delta, rate difference (group 1 - group 2) under the null hypothesis
    p.value:the p-value for the Farrington-Manning test
    null.value:rate difference (group 1 - group 2) under the null
    alternative:a character string indicating the alternative hypothesis
    method:a character string indicating the exact method employed
    data.name:a character string giving the names of the data used
    estimate:the estimated rate difference (maximum likelihood)
    conf.int:a confidence interval for the rate difference
    sample.size:the total sample size used for the test
    -
    -

    Details

    -

    The Farrington-Maning test for rate differences test the null hypothesis - of $$H_{0}: p_{1} - p_{2} = \delta$$ for the "two.sided" alternative - (or \(\geq\) for the "greater" respectively \(\leq\) for the "less" alternative). - This formulation allows to specify non-inferiority and superiority - test in a consistent manner:

    non-inferiority
    -

    for delta < 0 and alternative == "greater" the null hypothesis - reads \(H_{0}: p_{1} - p_{2} \geq \delta\) and - consequently rejection allows concluding that \(p_1 \geq p_2 + \delta\) - i.e. that the rate of success in group one is at least the - success rate in group two plus delta - as delta is negagtive this is equivalent to the success rate of group 1 - being at worst |delta| smaller than that of group 2.

    - -
    superiority
    -

    for delta >= 0 and alternative == "greater" the null hypothesis - reads \(H_{0}: p_{1} - p_{2} \geq \delta\) and - consequently rejection allows concluding that \(p_1 \geq p_2 + \delta\) - i.e. that the rate of success in group one is at least delta greater than the - success rate in group two.

    - - -

    The confidence interval is always computed as two-sided, but with 1-2\(\alpha\) confidence level -in case of a one-sided hypthesis. This means that the lower or upper vound are valid one-sided -confidence bounds at level \(\alpha\) in this case. -The confidence interval is constructed by inverting the two-sided test directly.

    -
    -
    -

    References

    -

    [1] Farrington, Conor P., and Godfrey Manning. "Test statistics and sample size formulae for comparative binomial trials with null hypothesis of non-zero risk difference or non-unity relative risk." Statistics in medicine 9.12 (1990): 1447-1454.

    -
    -
    -

    Author

    -

    Kevin Kunzmann

    -
    - -
    -

    Examples

    - 
    x <- c(rep(TRUE, 20), rep(FALSE, 15))
-y <- c(rep(TRUE, 30), rep(FALSE, 25))
-
-farrington.manning(x, y, -.3)
-#> 
-#> 	Farrington-Manning test for non-inferiority of rates
-#> 
-#> data:  group 1: x, group 2: y
-#> Z-statistic = 3.1546, noninferiority margin = -0.3, p-value = 0.0008037
-#> alternative hypothesis: true rate difference (group 1 - group 2) is greater than -0.3
-#> 95 percent confidence interval:
-#>  -0.1824742  0.2282376
-#> sample estimates:
-#> rate difference (group 1 - group 2) 
-#>                          0.02597403 
-#> 
-
-
    -
    -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/format_freqs.html b/docs/reference/format_freqs.html deleted file mode 100644 index cb38a94..0000000 --- a/docs/reference/format_freqs.html +++ /dev/null @@ -1,131 +0,0 @@ - -Formatting function for absolute and relative frequencies — format_freqs • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Formatting function for absolute and relative frequencies

    - Source: R/prettyDigits.R - -
    - -
    -

    Formatting function for absolute and relative frequencies

    -
    - -
    - 
    format_freqs(
-  numerator,
-  denominator = 1,
-  absolute_relative_frequency_mode = c("both", "only_absolute", "only_relative"),
-  percent_accuracy = NULL,
-  percent_suffix = "%"
-)
    -
    - -
    -

    Arguments

    -
    numerator
    -

    (numeric) numerator for % calculations

    -
    denominator
    -

    (numeric) denominator for % calculations

    -
    absolute_relative_frequency_mode
    -

    one of c("both", "only_absolute", "only_relative"). -"both" will print "Absolute Freq. (Relative Freq. %)", the other options work accordingly.

    -
    percent_accuracy
    -

    NULL or numeric. Refer to the accuracy argument in -percent.

    -
    percent_suffix
    -

    usually "%" or "". Refer to the suffix argument in -percent.

    -
    -
    -

    Value

    -

    string of formatted frequencies

    -
    - -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/ignore_unused_args.html b/docs/reference/ignore_unused_args.html deleted file mode 100644 index 3d88799..0000000 --- a/docs/reference/ignore_unused_args.html +++ /dev/null @@ -1,135 +0,0 @@ - -do.call but without an error for unused arguments — ignore_unused_args • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    do.call but without an error for unused arguments

    - Source: R/descr.R - -
    - -
    -

    do.call but without an error for unused arguments

    -
    - -
    - 
    ignore_unused_args(what, args)
    -
    - -
    -

    Arguments

    -
    what
    -

    either a function or a non-empty character string naming the function to be called.

    -
    args
    -

    a list of arguments to the function call. The names attribute of args gives the argument names.

    -
    -
    -

    Value

    -

    The result of the (evaluated) function call.

    -
    - -
    -

    Examples

    - 
    # works:
-DescrTab2:::ignore_unused_args(
-  chisq.test,
-  list(x = factor(c(1, 0, 1, 1, 1, 0)), y = factor(c(0, 1, 1, 0, 1, 0)), abc = 3)
-)
-#> Warning: Chi-squared approximation may be incorrect
-#> 
-#> 	Pearson's Chi-squared test
-#> 
-#> data:  structure(c(2L, 1L, 2L, 2L, 2L, 1L), .Label = c("0", "1"), class = "factor") and structure(c(1L, 2L, 2L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor")
-#> X-squared = 0, df = 1, p-value = 1
-#> 
-
-# would produce error:
-# do.call(chisq.test, list(x=factor(c(1,0,1,1,1,0)), y=factor(c(0,1,1,0,1,0)), abc=3 ) )
-
    -
    -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/inMinipage.html b/docs/reference/inMinipage.html deleted file mode 100644 index c3fc8ea..0000000 --- a/docs/reference/inMinipage.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -Wrap cell text in minipage LaTeX environment — inMinipage • DescrTab2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Wrap cell text in minipage LaTeX environment

    - Source: R/descr.R - -
    - -
    -

    Wrap cell text in minipage LaTeX environment

    -
    - - 
    inMinipage(x, width)
    - -

    Arguments

    - - - - - - - - - - -
    x

    text to be placed in minipage

    width

    width adjustment -https://stackoverflow.com/a/50892682

    - - -
    - -
    - - -
    -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/inMinipage2.html b/docs/reference/inMinipage2.html deleted file mode 100644 index 6423d59..0000000 --- a/docs/reference/inMinipage2.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -Wrap cell text in minipage LaTeX environment — inMinipage2 • DescrTab2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    Wrap cell text in minipage LaTeX environment

    - Source: R/descr.R - -
    - -
    -

    Wrap cell text in minipage LaTeX environment

    -
    - - 
    inMinipage2(x, width)
    - -

    Arguments

    - - - - - - - - - - -
    x

    text to be placed in minipage

    width

    width adjustment -https://stackoverflow.com/a/50892682

    - - -
    - -
    - - -
    -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/index.html b/docs/reference/index.html deleted file mode 100644 index de90167..0000000 --- a/docs/reference/index.html +++ /dev/null @@ -1,215 +0,0 @@ - -Function reference • DescrTab2 - - -
    -
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    Reference

    -
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -

    All functions

    -

    -
    -

    codegen_load_all_sas_data()

    -

    Create code to load all SAS datasets in a folder.

    -

    create_character_subtable()

    -

    Function to create (a part of a) nicely formatted table

    -

    create_numeric_subtable()

    -

    Function to create (a part of a) nicely formatted table

    -

    descr()

    -

    Calculate descriptive statistics

    -

    DescrTab2

    -

    DescrTab2

    -

    .onLoad()

    -

    Load LaTeX packages

    -

    escape_latex_symbols()

    -

    Escape LaTeX Symbols

    -

    extract_labels()

    -

    Extract the label attribute from data

    -

    farrington.manning()

    -

    Farrington-Manning test for rate difference

    -

    format_freqs()

    -

    Formatting function for absolute and relative frequencies

    -

    guess_ID_variable()

    -

    Make an educated guess about the name of the ID variable from a dataset

    -

    ignore_unused_args()

    -

    do.call but without an error for unused arguments

    -

    in_minipage()

    -

    Wrap cell text in minipage LaTeX environment with stretchy space

    -

    knit_print(<DescrList>)

    -

    S3 override for knit_print function for DescrList objects.

    -

    knit_print(<DescrPrint>)

    -

    S3 override for knit_print function for DescrPrint objects.

    -

    lapply_descr()

    -

    Convenience function to apply descr to a list of datasets and print the results

    -

    list_freetext_markdown()

    -

    Create a markdown listing from a character dataset

    -

    n_int_digits()

    -

    Digits before decimal -1

    -

    parse_formats()

    -

    Parse a text file containing format information

    -

    print(<DescrList>)

    -

    S3 override for print function for DescrList objects.

    -

    print_test_names()

    -

    Prints all possible tests names

    -

    read_redcap_formatted()

    -

    Convencience function to load datasets downloaded from a Redcap database

    -

    read_sas_formatted()

    -

    Convencience function to load SAS datasets

    -

    sigfig()

    -

    Format number to a specified number of digits, considering threshold for usage of scientific notation

    -

    sigfig_gen()

    -

    Generator function for nice formatting functions

    -

    sig_test()

    -

    Calculates a statistical significance test

    -

    split_redcap_dataset()

    -

    Split a dataset imported from Redcap into convenient subsets

    -

    unlabel()

    -

    Remove the label attribute from data

    -

    write_in_tmpfile_for_cran()

    -

    Function that returns true in CRAN submission

    - - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

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    -

    Site built with pkgdown 2.0.2.

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    - -
    - - - - - - - - diff --git a/docs/reference/print.DescrList.html b/docs/reference/print.DescrList.html deleted file mode 100644 index daac375..0000000 --- a/docs/reference/print.DescrList.html +++ /dev/null @@ -1,1179 +0,0 @@ - -S3 override for print function for DescrList objects. — print.DescrList • DescrTab2 - - -
    -
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    S3 override for print function for DescrList objects.

    - Source: R/descr.R - -
    - -
    -

    This function takes a DescrList object and converts it to either a DescrPrintCharacter or DescrPrintNumeric object, -depending on the print_format option. This object is then printed in an appropriate format.

    -
    - -
    - 
    # S3 method for DescrList
-print(x, print_format = options("print_format")[[1]], silent = FALSE, ...)
    -
    - -
    -

    Arguments

    -
    x
    -

    A DescrList object returned from descr.

    -
    print_format
    -

    Possible values: "console" (default), "tex", "html", "word", "numeric"

    -
    silent
    -

    I TRUE, suppresses output to stdout.

    -
    ...
    -

    further arguments to be passed along to print method

    -
    -
    -

    Value

    -

    A DescrPrint object which can be printed in various formats. -You can use the print_format option to control the output type. If you use 'DescrTab2' inside an .Rmd document, -you can set the clobal option option(print_format="tex") or option(print_format="html") or -option(print_format="word") depending on your document type. This way, all your tables will be printed in the -right format by default inside this document.

    -
    -
    -

    Details

    -

    There is no way to convert between DescrPrintCharacter and DescrPrintNumeric objects. The first type is for -what you would usually want, the second type is mostly for debugging purposes. A DescrPrintCharacter object can -be printed as html, tex code, as a flextable object or simply to the console.

    -
    - -
    -

    Examples

    - 
    print(descr(iris), print_format = "console")
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-print(descr(iris), print_format = "tex")
-#> \needspace{2cm}
-#> 
-#> \begin{longtable}[t]{>{\raggedright\arraybackslash}p{7em}cc}
-#> \toprule
-#> \multicolumn{1}{l}{Variables} & \multicolumn{1}{c}{Total} & \multicolumn{1}{c}{p} \\*
-#>  & (N=150) & \\*
-#> \midrule
-#> \endfirsthead
-#> \multicolumn{3}{@{}l}{\textit{(continued)}}\\*
-#> \toprule
-#> \multicolumn{1}{l}{Variables} & \multicolumn{1}{c}{Total} & \multicolumn{1}{c}{p} \\*
-#>  & (N=150) & \\*
-#> \midrule
-#> \endhead
-#> 
-#> \endfoot
-#> 
-#> \endlastfoot
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Sepal.Length\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{3} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 5.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.83 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 5.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 5.1 -- 6.4 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 4.3 -- 7.9 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Sepal.Width\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{2} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 3.1 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.44 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 2.8 -- 3.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 2 -- 4.4 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Petal.Length\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{1} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 3.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 1.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 4.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 1.6 -- 5.1 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 1 -- 6.9 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Petal.Width\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 1.2 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.76 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 1.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 0.3 -- 1.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 0.1 -- 2.5 & \\ \noalign{\vskip 0pt plus 12pt} \pagebreak[3]
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Species\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright setosa\end{minipage} & 50 (33\%) & \textgreater0.999\textsuperscript{chi1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright versicolor\end{minipage} & 50 (33\%) & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright virginica\end{minipage} & 50 (33\%) & \\*
-#> \bottomrule
-#> \multicolumn{3}{l}{\rule{0pt}{1em}\textsuperscript{tt1} Student's one-sample t-test}\\*
-#> \multicolumn{3}{l}{\rule{0pt}{1em}\textsuperscript{chi1} Chi-squared goodness-of-fit test}\\*
-#> \end{longtable}
-print(descr(iris), print_format = "html")
-#> <table class="table" style="margin-left: auto; margin-right: auto;border-bottom: 0;">
-#>  <thead>
-#> <tr>
-#> <th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: left; " colspan="1"><div style="">Variables</div></th>
-#> <th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1"><div style="">Total</div></th>
-#> <th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1"><div style="">p</div></th>
-#> </tr>
-#>   <tr>
-#>    <th style="text-align:left;">  </th>
-#>    <th style="text-align:center;"> (N=150) </th>
-#>    <th style="text-align:center;">  </th>
-#>   </tr>
-#>  </thead>
-#> <tbody>
-#>   <tr grouplength="6"><td colspan="3" style="border-bottom: 1px solid;"><strong>Sepal.Length</strong></td></tr>
-#> <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> N </td>
-#>    <td style="text-align:center;"> 150 </td>
-#>    <td style="text-align:center;"> &lt;0.001<sup>tt1</sup> </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> mean </td>
-#>    <td style="text-align:center;"> 5.8 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> sd </td>
-#>    <td style="text-align:center;"> 0.83 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> median </td>
-#>    <td style="text-align:center;"> 5.8 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> Q1 - Q3 </td>
-#>    <td style="text-align:center;"> 5.1 -- 6.4 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> min - max </td>
-#>    <td style="text-align:center;"> 4.3 -- 7.9 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr grouplength="6"><td colspan="3" style="border-bottom: 1px solid;"><strong>Sepal.Width</strong></td></tr>
-#> <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> N </td>
-#>    <td style="text-align:center;"> 150 </td>
-#>    <td style="text-align:center;"> &lt;0.001<sup>tt1</sup> </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> mean </td>
-#>    <td style="text-align:center;"> 3.1 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> sd </td>
-#>    <td style="text-align:center;"> 0.44 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> median </td>
-#>    <td style="text-align:center;"> 3 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> Q1 - Q3 </td>
-#>    <td style="text-align:center;"> 2.8 -- 3.3 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> min - max </td>
-#>    <td style="text-align:center;"> 2 -- 4.4 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr grouplength="6"><td colspan="3" style="border-bottom: 1px solid;"><strong>Petal.Length</strong></td></tr>
-#> <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> N </td>
-#>    <td style="text-align:center;"> 150 </td>
-#>    <td style="text-align:center;"> &lt;0.001<sup>tt1</sup> </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> mean </td>
-#>    <td style="text-align:center;"> 3.8 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> sd </td>
-#>    <td style="text-align:center;"> 1.8 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> median </td>
-#>    <td style="text-align:center;"> 4.3 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> Q1 - Q3 </td>
-#>    <td style="text-align:center;"> 1.6 -- 5.1 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> min - max </td>
-#>    <td style="text-align:center;"> 1 -- 6.9 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr grouplength="6"><td colspan="3" style="border-bottom: 1px solid;"><strong>Petal.Width</strong></td></tr>
-#> <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> N </td>
-#>    <td style="text-align:center;"> 150 </td>
-#>    <td style="text-align:center;"> &lt;0.001<sup>tt1</sup> </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> mean </td>
-#>    <td style="text-align:center;"> 1.2 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> sd </td>
-#>    <td style="text-align:center;"> 0.76 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> median </td>
-#>    <td style="text-align:center;"> 1.3 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> Q1 - Q3 </td>
-#>    <td style="text-align:center;"> 0.3 -- 1.8 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> min - max </td>
-#>    <td style="text-align:center;"> 0.1 -- 2.5 </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr grouplength="3"><td colspan="3" style="border-bottom: 1px solid;"><strong>Species</strong></td></tr>
-#> <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> setosa </td>
-#>    <td style="text-align:center;"> 50 (33%) </td>
-#>    <td style="text-align:center;"> &gt;0.999<sup>chi1</sup> </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> versicolor </td>
-#>    <td style="text-align:center;"> 50 (33%) </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#>   <tr>
-#>    <td style="text-align:left;width: 4.2cm; padding-left: 2em;" indentlevel="1"> virginica </td>
-#>    <td style="text-align:center;"> 50 (33%) </td>
-#>    <td style="text-align:center;">  </td>
-#>   </tr>
-#> </tbody>
-#> <tfoot>
-#> <tr><td style="padding: 0; " colspan="100%">
-#> <sup>tt1</sup> Student's one-sample t-test</td></tr>
-#> <tr><td style="padding: 0; " colspan="100%">
-#> <sup>chi1</sup> Chi-squared goodness-of-fit test</td></tr>
-#> </tfoot>
-#> </table>
-print(descr(iris), print_format = "word")
-print(descr(iris), print_format = "numeric")
-#> # A tibble: 44 x 4
-#>    Variable         Total          p Test                            
-#>    <chr>            <dbl>      <dbl> <chr>                           
-#>  1  Sepal.Length           3.33e-129 Student's one-sample t-test     
-#>  2    N           150                                                
-#>  3    Nmiss         0                                                
-#>  4    mean          5.84                                             
-#>  5    sd            0.828                                            
-#>  6    median        5.8                                              
-#>  7    Q1            5.1                                              
-#>  8    Q3            6.4                                              
-#>  9    min           4.3                                              
-#> 10    max           7.9                                              
-#> 11  Sepal.Width            8.00e-129 Student's one-sample t-test     
-#> 12    N           150                                                
-#> 13    Nmiss         0                                                
-#> 14    mean          3.06                                             
-#> 15    sd            0.436                                            
-#> 16    median        3                                                
-#> 17    Q1            2.8                                              
-#> 18    Q3            3.3                                              
-#> 19    min           2                                                
-#> 20    max           4.4                                              
-#> 21  Petal.Length           2.17e- 57 Student's one-sample t-test     
-#> 22    N           150                                                
-#> 23    Nmiss         0                                                
-#> 24    mean          3.76                                             
-#> 25    sd            1.77                                             
-#> 26    median        4.35                                             
-#> 27    Q1            1.6                                              
-#> 28    Q3            5.1                                              
-#> 29    min           1                                                
-#> 30    max           6.9                                              
-#> 31  Petal.Width            2.66e- 42 Student's one-sample t-test     
-#> 32    N           150                                                
-#> 33    Nmiss         0                                                
-#> 34    mean          1.20                                             
-#> 35    sd            0.762                                            
-#> 36    median        1.3                                              
-#> 37    Q1            0.3                                              
-#> 38    Q3            1.8                                              
-#> 39    min           0.1                                              
-#> 40    max           2.5                                              
-#> 41  Species                1   e+  0 Chi-squared goodness-of-fit test
-#> 42    setosa       50                                                
-#> 43    versicolor   50                                                
-#> 44    virginica    50                                                
-options(print_format = "tex")
-descr(iris)
-#> \needspace{2cm}
-#> 
-#> \begin{longtable}[t]{>{\raggedright\arraybackslash}p{7em}cc}
-#> \toprule
-#> \multicolumn{1}{l}{Variables} & \multicolumn{1}{c}{Total} & \multicolumn{1}{c}{p} \\*
-#>  & (N=150) & \\*
-#> \midrule
-#> \endfirsthead
-#> \multicolumn{3}{@{}l}{\textit{(continued)}}\\*
-#> \toprule
-#> \multicolumn{1}{l}{Variables} & \multicolumn{1}{c}{Total} & \multicolumn{1}{c}{p} \\*
-#>  & (N=150) & \\*
-#> \midrule
-#> \endhead
-#> 
-#> \endfoot
-#> 
-#> \endlastfoot
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Sepal.Length\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{3} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 5.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.83 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 5.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 5.1 -- 6.4 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 4.3 -- 7.9 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Sepal.Width\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{2} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 3.1 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.44 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 2.8 -- 3.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 2 -- 4.4 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Petal.Length\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \vphantom{1} \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 3.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 1.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 4.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 1.6 -- 5.1 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 1 -- 6.9 & \\ \noalign{\vskip 0pt plus 12pt}
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Petal.Width\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright N\end{minipage} & 150 & \textless0.001\textsuperscript{tt1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright mean\end{minipage} & 1.2 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright sd\end{minipage} & 0.76 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright median\end{minipage} & 1.3 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright Q1 - Q3\end{minipage} & 0.3 -- 1.8 & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright min - max\end{minipage} & 0.1 -- 2.5 & \\ \noalign{\vskip 0pt plus 12pt} \pagebreak[3]
-#> \addlinespace[0.5cm]
-#> \multicolumn{3}{l}{\textbf{\begin{minipage}[t]{7em}\raggedright Species\end{minipage}}}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright setosa\end{minipage} & 50 (33\%) & \textgreater0.999\textsuperscript{chi1}\\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright versicolor\end{minipage} & 50 (33\%) & \\*
-#> \hspace{1em}\begin{minipage}[t]{6em}\raggedright virginica\end{minipage} & 50 (33\%) & \\*
-#> \bottomrule
-#> \multicolumn{3}{l}{\rule{0pt}{1em}\textsuperscript{tt1} Student's one-sample t-test}\\*
-#> \multicolumn{3}{l}{\rule{0pt}{1em}\textsuperscript{chi1} Chi-squared goodness-of-fit test}\\*
-#> \end{longtable}
-options(print_format = "console")
-descr(iris)
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-DescrPrint <- print(descr(iris))
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-DescrPrint$variables$results$Sepal.Length$Total$mean
-#> NULL
-print(DescrPrint)
-#> $variables
-#> $variables$Sepal.Length
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "5.8"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.83"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "5.8"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "5.1 -- 6.4"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "4.3 -- 7.9"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 3.331256e-129
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Sepal.Length"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Sepal.Length"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Sepal.Length"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Sepal.Width
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "3.1"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.44"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "2.8 -- 3.3"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "2 -- 4.4"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 8.004458e-129
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Sepal.Width"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Sepal.Width"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Sepal.Width"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Petal.Length
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "3.8"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "1.8"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "4.3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "1.6 -- 5.1"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "1 -- 6.9"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 2.166017e-57
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Petal.Length"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Petal.Length"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Petal.Length"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Petal.Width
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "1.2"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.76"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "1.3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "0.3 -- 1.8"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "0.1 -- 2.5"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 2.659021e-42
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Petal.Width"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Petal.Width"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Petal.Width"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Species
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> list()
-#> 
-#> $results$Total$categories
-#> $results$Total$categories$setosa
-#> [1] "50 (33%)"
-#> 
-#> $results$Total$categories$versicolor
-#> [1] "50 (33%)"
-#> 
-#> $results$Total$categories$virginica
-#> [1] "50 (33%)"
-#> 
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 1
-#> 
-#> $test_list$test_name
-#> [1] "Chi-squared goodness-of-fit test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Species"
-#> 
-#> $variable_levels
-#> [1] "setosa"     "versicolor" "virginica" 
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Species"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Species"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> 
-#> $lengths
-#> $lengths$Sepal.Length
-#> [1] 7
-#> 
-#> $lengths$Sepal.Width
-#> [1] 7
-#> 
-#> $lengths$Petal.Length
-#> [1] 7
-#> 
-#> $lengths$Petal.Width
-#> [1] 7
-#> 
-#> $lengths$Species
-#> [1] 4
-#> 
-#> 
-#> $group
-#> $group$labels
-#> list()
-#> 
-#> $group$lengths
-#> Total 
-#>   150 
-#> 
-#> 
-#> $group_names
-#> [1] "Total"
-#> 
-#> $format
-#> $format$p
-#> function (x) 
-#> pvalue(x, accuracy = accuracy, decimal.mark = decimal.mark, prefix = prefix, 
-#>     add_p = add_p)
-#> <bytecode: 0x0000000043da5f08>
-#> <environment: 0x0000000052aa5b20>
-#> 
-#> $format$summary_stats
-#> $format$summary_stats$N
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$mean
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$sd
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$median
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$Q1
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$Q3
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$min
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$max
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> $format$summary_stats$CI
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> 
-#> $format$options
-#> $format$options$print_Total
-#> [1] TRUE
-#> 
-#> $format$options$print_p
-#> [1] TRUE
-#> 
-#> $format$options$print_CI
-#> [1] TRUE
-#> 
-#> $format$options$combine_mean_sd
-#> [1] FALSE
-#> 
-#> $format$options$combine_median_Q1_Q3
-#> [1] FALSE
-#> 
-#> $format$options$omit_factor_level
-#> [1] "none"
-#> 
-#> $format$options$omit_Nmiss_if_0
-#> [1] TRUE
-#> 
-#> $format$options$omit_missings_in_group
-#> [1] TRUE
-#> 
-#> $format$options$percent_accuracy
-#> NULL
-#> 
-#> $format$options$percent_suffix
-#> [1] "%"
-#> 
-#> $format$options$row_percent
-#> [1] FALSE
-#> 
-#> $format$options$Nmiss_row_percent
-#> [1] FALSE
-#> 
-#> $format$options$absolute_relative_frequency_mode
-#> [1] "both"          "only_absolute" "only_relative"
-#> 
-#> $format$options$omit_missings_in_categorical_var
-#> [1] FALSE
-#> 
-#> $format$options$categorical_missing_percent_mode
-#> [1] "no_missing_percent"           "missing_as_regular_category" 
-#> [3] "missing_as_separate_category"
-#> 
-#> $format$options$caption
-#> NULL
-#> 
-#> $format$options$replace_empty_string_with_NA
-#> [1] TRUE
-#> 
-#> $format$options$categories_first_summary_stats_second
-#> [1] FALSE
-#> 
-#> 
-#> $format$reshape_rows
-#> $format$reshape_rows$`Q1 - Q3`
-#> $format$reshape_rows$`Q1 - Q3`$args
-#> [1] "Q1" "Q3"
-#> 
-#> $format$reshape_rows$`Q1 - Q3`$fun
-#> function (Q1, Q3) 
-#> {
-#>     paste0(Q1, " -- ", Q3)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> 
-#> $format$reshape_rows$`min - max`
-#> $format$reshape_rows$`min - max`$args
-#> [1] "min" "max"
-#> 
-#> $format$reshape_rows$`min - max`$fun
-#> function (min, max) 
-#> {
-#>     paste0(min, " -- ", max)
-#> }
-#> <environment: 0x000000005281fac8>
-#> 
-#> 
-#> 
-#> $format$test_abbreviations
-#> $format$test_abbreviations$`Cochran's Q test`
-#> [1] "CocQ"
-#> 
-#> $format$test_abbreviations$`McNemar's test`
-#> [1] "McN"
-#> 
-#> $format$test_abbreviations$`Chi-squared goodness-of-fit test`
-#> [1] "chi1"
-#> 
-#> $format$test_abbreviations$`Pearson's chi-squared test`
-#> [1] "chi2"
-#> 
-#> $format$test_abbreviations$`Exact McNemar's test`
-#> [1] "eMcN"
-#> 
-#> $format$test_abbreviations$`Boschloo's test`
-#> [1] "Bolo"
-#> 
-#> $format$test_abbreviations$`Exact binomial test`
-#> [1] "Bin"
-#> 
-#> $format$test_abbreviations$`Fisher's exact test`
-#> [1] "Fish"
-#> 
-#> $format$test_abbreviations$`Friedman test`
-#> [1] "Frie"
-#> 
-#> $format$test_abbreviations$`Wilcoxon two-sample signed-rank test`
-#> [1] "Wil2"
-#> 
-#> $format$test_abbreviations$`Wilcoxon's one-sample signed-rank test`
-#> [1] "Wil1"
-#> 
-#> $format$test_abbreviations$`Mann-Whitney's U test`
-#> [1] "MWU"
-#> 
-#> $format$test_abbreviations$`Kruskal-Wallis's one-way ANOVA`
-#> [1] "KW"
-#> 
-#> $format$test_abbreviations$`Student's paired t-test`
-#> [1] "tpar"
-#> 
-#> $format$test_abbreviations$`Mixed model ANOVA`
-#> [1] "MiAn"
-#> 
-#> $format$test_abbreviations$`Student's one-sample t-test`
-#> [1] "tt1"
-#> 
-#> $format$test_abbreviations$`Welch's two-sample t-test`
-#> [1] "tt2"
-#> 
-#> $format$test_abbreviations$`F-test (ANOVA)`
-#> [1] "F"
-#> 
-#> $format$test_abbreviations$`Cochran-Armitage's test`
-#> [1] "CocA"
-#> 
-#> $format$test_abbreviations$`Jonckheere-Terpstra's test`
-#> [1] "JT"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from a normal ("Wald") approximation`
-#> [1] "PWa"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from an unconditional exact test`
-#> [1] "PUnc"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from an exact McNemar's test`
-#> [1] "PMcN"
-#> 
-#> $format$test_abbreviations$`CI for difference in means derived from the t-distribution`
-#> [1] "t"
-#> 
-#> $format$test_abbreviations$`CI for the Hodges-Lehmann estimator`
-#> [1] "HL"
-#> 
-#> $format$test_abbreviations$`CI for odds ratio derived from Fisher's exact test`
-#> [1] "Odds"
-#> 
-#> $format$test_abbreviations$`No test`
-#> [1] "NA"
-#> 
-#> 
-#> 
-#> $group_labels
-#> [1] "Total"
-#> 
-#> $labels
-#> $labels$Sepal.Length
-#> [1] "Sepal.Length"
-#> 
-#> $labels$Sepal.Width
-#> [1] "Sepal.Width"
-#> 
-#> $labels$Petal.Length
-#> [1] "Petal.Length"
-#> 
-#> $labels$Petal.Width
-#> [1] "Petal.Width"
-#> 
-#> $labels$Species
-#> [1] "Species"
-#> 
-#> 
-#> $tibble
-#> # A tibble: 32 x 4
-#>    Variables    Total        p        Test                         
-#>    <chr>        <chr>        <chr>    <chr>                        
-#>  1 Sepal.Length ""           ""       ""                           
-#>  2 N            "150"        "<0.001" "Student's one-sample t-test"
-#>  3 mean         "5.8"        ""       ""                           
-#>  4 sd           "0.83"       ""       ""                           
-#>  5 median       "5.8"        ""       ""                           
-#>  6 Q1 - Q3      "5.1 -- 6.4" ""       ""                           
-#>  7 min - max    "4.3 -- 7.9" ""       ""                           
-#>  8 Sepal.Width  ""           ""       ""                           
-#>  9 N            "150"        "<0.001" "Student's one-sample t-test"
-#> 10 mean         "3.1"        ""       ""                           
-#> # ... with 22 more rows
-#> 
-#> attr(,"class")
-#> [1] "DescrPrint"          "DescrPrintCharacter" "list"               
-
    -
    -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/print.DescrPrint.html b/docs/reference/print.DescrPrint.html deleted file mode 100644 index 880de7c..0000000 --- a/docs/reference/print.DescrPrint.html +++ /dev/null @@ -1,799 +0,0 @@ - -S3 override for print function for DescrPrint objects — print.DescrPrint • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    S3 override for print function for DescrPrint objects

    - Source: R/descr.R - -
    - -
    -

    S3 override for print function for DescrPrint objects

    -
    - -
    - 
    # S3 method for DescrPrint
-print(x, print_format = options("print_format")[[1]], silent = FALSE, ...)
    -
    - -
    -

    Arguments

    -
    x
    -

    A DescrList object returned from descr.

    -
    print_format
    -

    Possible values: "console" (default), "tex", "html", "word", "numeric"

    -
    silent
    -

    I TRUE, suppresses output to stdout.

    -
    ...
    -

    further arguments to be passed along to print method

    -
    -
    -

    Value

    -

    A DescrPrint object which can be printed in various formats.

    -
    - -
    -

    Examples

    - 
    descr(iris)
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-DescrPrint <- print(descr(iris))
-#>    Variables      Total        p        Test                              
-#>  1  Sepal.Length                                                          
-#>  2    N            150          <0.001   Student's one-sample t-test      
-#>  3    mean         5.8                                                    
-#>  4    sd           0.83                                                   
-#>  5    median       5.8                                                    
-#>  6    Q1 - Q3      5.1 -- 6.4                                             
-#>  7    min - max    4.3 -- 7.9                                             
-#>  8  Sepal.Width                                                           
-#>  9    N            150          <0.001   Student's one-sample t-test      
-#> 10    mean         3.1                                                    
-#> 11    sd           0.44                                                   
-#> 12    median       3                                                      
-#> 13    Q1 - Q3      2.8 -- 3.3                                             
-#> 14    min - max    2 -- 4.4                                               
-#> 15  Petal.Length                                                          
-#> 16    N            150          <0.001   Student's one-sample t-test      
-#> 17    mean         3.8                                                    
-#> 18    sd           1.8                                                    
-#> 19    median       4.3                                                    
-#> 20    Q1 - Q3      1.6 -- 5.1                                             
-#> 21    min - max    1 -- 6.9                                               
-#> 22  Petal.Width                                                           
-#> 23    N            150          <0.001   Student's one-sample t-test      
-#> 24    mean         1.2                                                    
-#> 25    sd           0.76                                                   
-#> 26    median       1.3                                                    
-#> 27    Q1 - Q3      0.3 -- 1.8                                             
-#> 28    min - max    0.1 -- 2.5                                             
-#> 29  Species                                                               
-#> 30    setosa       50 (33%)     >0.999   Chi-squared goodness-of-fit test 
-#> 31    versicolor   50 (33%)                                               
-#> 32    virginica    50 (33%)                                               
-DescrPrint$variables$results$Sepal.Length$Total$mean
-#> NULL
-print(DescrPrint)
-#> $variables
-#> $variables$Sepal.Length
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "5.8"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.83"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "5.8"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "5.1 -- 6.4"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "4.3 -- 7.9"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 3.331256e-129
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Sepal.Length"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Sepal.Length"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Sepal.Length"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Sepal.Width
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "3.1"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.44"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "2.8 -- 3.3"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "2 -- 4.4"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 8.004458e-129
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Sepal.Width"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Sepal.Width"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Sepal.Width"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Petal.Length
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "3.8"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "1.8"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "4.3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "1.6 -- 5.1"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "1 -- 6.9"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 2.166017e-57
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Petal.Length"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Petal.Length"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Petal.Length"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Petal.Width
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> $results$Total$summary_stats$N
-#> [1] "150"
-#> 
-#> $results$Total$summary_stats$mean
-#> [1] "1.2"
-#> 
-#> $results$Total$summary_stats$sd
-#> [1] "0.76"
-#> 
-#> $results$Total$summary_stats$median
-#> [1] "1.3"
-#> 
-#> $results$Total$summary_stats$`Q1 - Q3`
-#> [1] "0.3 -- 1.8"
-#> 
-#> $results$Total$summary_stats$`min - max`
-#> [1] "0.1 -- 2.5"
-#> 
-#> 
-#> $results$Total$categories
-#> list()
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 2.659021e-42
-#> 
-#> $test_list$test_name
-#> [1] "Student's one-sample t-test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Petal.Width"
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Petal.Width"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Petal.Width"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> $variables$Species
-#> $results
-#> $results$Total
-#> $results$Total$summary_stats
-#> list()
-#> 
-#> $results$Total$categories
-#> $results$Total$categories$setosa
-#> [1] "50 (33%)"
-#> 
-#> $results$Total$categories$versicolor
-#> [1] "50 (33%)"
-#> 
-#> $results$Total$categories$virginica
-#> [1] "50 (33%)"
-#> 
-#> 
-#> 
-#> 
-#> $test_list
-#> $test_list$p
-#> [1] 1
-#> 
-#> $test_list$test_name
-#> [1] "Chi-squared goodness-of-fit test"
-#> 
-#> 
-#> $variable_name
-#> [1] "Species"
-#> 
-#> $variable_levels
-#> [1] "setosa"     "versicolor" "virginica" 
-#> 
-#> $variable_options
-#> $variable_options$label
-#> [1] "Species"
-#> 
-#> 
-#> $variable_lengths
-#> $variable_lengths$Total
-#> $variable_lengths$Total$N
-#> [1] 150
-#> 
-#> $variable_lengths$Total$Nmiss
-#> [1] 0
-#> 
-#> 
-#> 
-#> $label
-#> [1] "Species"
-#> 
-#> attr(,"class")
-#> [1] "summary_stats" "list"         
-#> 
-#> 
-#> $lengths
-#> $lengths$Sepal.Length
-#> [1] 7
-#> 
-#> $lengths$Sepal.Width
-#> [1] 7
-#> 
-#> $lengths$Petal.Length
-#> [1] 7
-#> 
-#> $lengths$Petal.Width
-#> [1] 7
-#> 
-#> $lengths$Species
-#> [1] 4
-#> 
-#> 
-#> $group
-#> $group$labels
-#> list()
-#> 
-#> $group$lengths
-#> Total 
-#>   150 
-#> 
-#> 
-#> $group_names
-#> [1] "Total"
-#> 
-#> $format
-#> $format$p
-#> function (x) 
-#> pvalue(x, accuracy = accuracy, decimal.mark = decimal.mark, prefix = prefix, 
-#>     add_p = add_p)
-#> <bytecode: 0x0000000043da5f08>
-#> <environment: 0x000000004f25d6f8>
-#> 
-#> $format$summary_stats
-#> $format$summary_stats$N
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$mean
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$sd
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$median
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$Q1
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$Q3
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$min
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$max
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> $format$summary_stats$CI
-#> function (x) 
-#> {
-#>     format(x, digits = 2, scientific = 3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> 
-#> $format$options
-#> $format$options$print_Total
-#> [1] TRUE
-#> 
-#> $format$options$print_p
-#> [1] TRUE
-#> 
-#> $format$options$print_CI
-#> [1] TRUE
-#> 
-#> $format$options$combine_mean_sd
-#> [1] FALSE
-#> 
-#> $format$options$combine_median_Q1_Q3
-#> [1] FALSE
-#> 
-#> $format$options$omit_factor_level
-#> [1] "none"
-#> 
-#> $format$options$omit_Nmiss_if_0
-#> [1] TRUE
-#> 
-#> $format$options$omit_missings_in_group
-#> [1] TRUE
-#> 
-#> $format$options$percent_accuracy
-#> NULL
-#> 
-#> $format$options$percent_suffix
-#> [1] "%"
-#> 
-#> $format$options$row_percent
-#> [1] FALSE
-#> 
-#> $format$options$Nmiss_row_percent
-#> [1] FALSE
-#> 
-#> $format$options$absolute_relative_frequency_mode
-#> [1] "both"          "only_absolute" "only_relative"
-#> 
-#> $format$options$omit_missings_in_categorical_var
-#> [1] FALSE
-#> 
-#> $format$options$categorical_missing_percent_mode
-#> [1] "no_missing_percent"           "missing_as_regular_category" 
-#> [3] "missing_as_separate_category"
-#> 
-#> $format$options$caption
-#> NULL
-#> 
-#> $format$options$replace_empty_string_with_NA
-#> [1] TRUE
-#> 
-#> $format$options$categories_first_summary_stats_second
-#> [1] FALSE
-#> 
-#> 
-#> $format$reshape_rows
-#> $format$reshape_rows$`Q1 - Q3`
-#> $format$reshape_rows$`Q1 - Q3`$args
-#> [1] "Q1" "Q3"
-#> 
-#> $format$reshape_rows$`Q1 - Q3`$fun
-#> function (Q1, Q3) 
-#> {
-#>     paste0(Q1, " -- ", Q3)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> 
-#> $format$reshape_rows$`min - max`
-#> $format$reshape_rows$`min - max`$args
-#> [1] "min" "max"
-#> 
-#> $format$reshape_rows$`min - max`$fun
-#> function (min, max) 
-#> {
-#>     paste0(min, " -- ", max)
-#> }
-#> <environment: 0x000000004efd3860>
-#> 
-#> 
-#> 
-#> $format$test_abbreviations
-#> $format$test_abbreviations$`Cochran's Q test`
-#> [1] "CocQ"
-#> 
-#> $format$test_abbreviations$`McNemar's test`
-#> [1] "McN"
-#> 
-#> $format$test_abbreviations$`Chi-squared goodness-of-fit test`
-#> [1] "chi1"
-#> 
-#> $format$test_abbreviations$`Pearson's chi-squared test`
-#> [1] "chi2"
-#> 
-#> $format$test_abbreviations$`Exact McNemar's test`
-#> [1] "eMcN"
-#> 
-#> $format$test_abbreviations$`Boschloo's test`
-#> [1] "Bolo"
-#> 
-#> $format$test_abbreviations$`Exact binomial test`
-#> [1] "Bin"
-#> 
-#> $format$test_abbreviations$`Fisher's exact test`
-#> [1] "Fish"
-#> 
-#> $format$test_abbreviations$`Friedman test`
-#> [1] "Frie"
-#> 
-#> $format$test_abbreviations$`Wilcoxon two-sample signed-rank test`
-#> [1] "Wil2"
-#> 
-#> $format$test_abbreviations$`Wilcoxon's one-sample signed-rank test`
-#> [1] "Wil1"
-#> 
-#> $format$test_abbreviations$`Mann-Whitney's U test`
-#> [1] "MWU"
-#> 
-#> $format$test_abbreviations$`Kruskal-Wallis's one-way ANOVA`
-#> [1] "KW"
-#> 
-#> $format$test_abbreviations$`Student's paired t-test`
-#> [1] "tpar"
-#> 
-#> $format$test_abbreviations$`Mixed model ANOVA`
-#> [1] "MiAn"
-#> 
-#> $format$test_abbreviations$`Student's one-sample t-test`
-#> [1] "tt1"
-#> 
-#> $format$test_abbreviations$`Welch's two-sample t-test`
-#> [1] "tt2"
-#> 
-#> $format$test_abbreviations$`F-test (ANOVA)`
-#> [1] "F"
-#> 
-#> $format$test_abbreviations$`Cochran-Armitage's test`
-#> [1] "CocA"
-#> 
-#> $format$test_abbreviations$`Jonckheere-Terpstra's test`
-#> [1] "JT"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from a normal ("Wald") approximation`
-#> [1] "PWa"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from an unconditional exact test`
-#> [1] "PUnc"
-#> 
-#> $format$test_abbreviations$`CI for difference in proportions derived from an exact McNemar's test`
-#> [1] "PMcN"
-#> 
-#> $format$test_abbreviations$`CI for difference in means derived from the t-distribution`
-#> [1] "t"
-#> 
-#> $format$test_abbreviations$`CI for the Hodges-Lehmann estimator`
-#> [1] "HL"
-#> 
-#> $format$test_abbreviations$`CI for odds ratio derived from Fisher's exact test`
-#> [1] "Odds"
-#> 
-#> $format$test_abbreviations$`No test`
-#> [1] "NA"
-#> 
-#> 
-#> 
-#> $group_labels
-#> [1] "Total"
-#> 
-#> $labels
-#> $labels$Sepal.Length
-#> [1] "Sepal.Length"
-#> 
-#> $labels$Sepal.Width
-#> [1] "Sepal.Width"
-#> 
-#> $labels$Petal.Length
-#> [1] "Petal.Length"
-#> 
-#> $labels$Petal.Width
-#> [1] "Petal.Width"
-#> 
-#> $labels$Species
-#> [1] "Species"
-#> 
-#> 
-#> $tibble
-#> # A tibble: 32 x 4
-#>    Variables    Total        p        Test                         
-#>    <chr>        <chr>        <chr>    <chr>                        
-#>  1 Sepal.Length ""           ""       ""                           
-#>  2 N            "150"        "<0.001" "Student's one-sample t-test"
-#>  3 mean         "5.8"        ""       ""                           
-#>  4 sd           "0.83"       ""       ""                           
-#>  5 median       "5.8"        ""       ""                           
-#>  6 Q1 - Q3      "5.1 -- 6.4" ""       ""                           
-#>  7 min - max    "4.3 -- 7.9" ""       ""                           
-#>  8 Sepal.Width  ""           ""       ""                           
-#>  9 N            "150"        "<0.001" "Student's one-sample t-test"
-#> 10 mean         "3.1"        ""       ""                           
-#> # ... with 22 more rows
-#> 
-#> attr(,"class")
-#> [1] "DescrPrint"          "DescrPrintCharacter" "list"               
-
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    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

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    Site built with pkgdown 2.0.2.

    -
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    - - - - - - - - diff --git a/docs/reference/print_test_names.html b/docs/reference/print_test_names.html deleted file mode 100644 index 2555c77..0000000 --- a/docs/reference/print_test_names.html +++ /dev/null @@ -1,135 +0,0 @@ - -Prints all possible tests names — print_test_names • DescrTab2 - - -
    -
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    Prints all possible tests names

    - Source: R/descr.R - -
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    Prints all possible tests names

    -
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    print_test_names()
    -
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    Value

    -

    Returns the names of all possible test names you can specify.

    -
    - -
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    Examples

    - 
    print_test_names()
-#>  [1] "Cochran's Q test"                      
-#>  [2] "McNemar's test"                        
-#>  [3] "Chi-squared goodness-of-fit test"      
-#>  [4] "Pearson's chi-squared test"            
-#>  [5] "Exact McNemar's test"                  
-#>  [6] "Boschloo's test"                       
-#>  [7] "Exact binomial test"                   
-#>  [8] "Fisher's exact test"                   
-#>  [9] "Exact binomal test"                    
-#> [10] "Friedman test"                         
-#> [11] "Wilcoxon two-sample signed-rank test"  
-#> [12] "Wilcoxon's one-sample signed-rank test"
-#> [13] "Mann-Whitney's U test"                 
-#> [14] "Kruskal-Wallis's one-way ANOVA"        
-#> [15] "Student's paired t-test"               
-#> [16] "Mixed model ANOVA"                     
-#> [17] "Student's one-sample t-test"           
-#> [18] "Welch's two-sample t-test"             
-#> [19] "Cochran-Armitage's test"               
-#> [20] "Jonckheere-Terpstra's test"            
-#> [21] "F-test (ANOVA)"                        
-
    -
    -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/reference/test_cat.html b/docs/reference/test_cat.html deleted file mode 100644 index 231927f..0000000 --- a/docs/reference/test_cat.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Calculate a statistical test for a categorical variable. — test_cat • DescrTab2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Calculate a statistical test for a categorical variable.

    - Source: R/descr.R - -
    - -
    -

    Calculate a statistical test for a categorical variable.

    -
    - - 
    test_cat(
-  var,
-  group = NULL,
-  test_options = list(),
-  test = NULL,
-  var_name = NULL
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    var

    A variable (a vector).

    group

    A variable containing the grouping information.

    test_options

    Named list containing test options.

    test

    Name of a statistical test.

    var_name

    Name of variable to be tested (only used in warning messages).

    - -

    Value

    - -

    A list of test test results.

    - -

    Examples

    - 
    cat_var <- factor(c("a", "b", "c"))
-test_cat(cat_var)
-
    #> Warning: Chi-squared approximation may be incorrect
    #> $p
-#> [1] 1
-#> 
-#> $test_name
-#> [1] "Chi-squared goodness-of-fit test"
-#>
    -
    - -
    - - -
    -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

    -
    - -
    -

    Site built with pkgdown 1.6.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/test_cont.html b/docs/reference/test_cont.html deleted file mode 100644 index 8fb253c..0000000 --- a/docs/reference/test_cont.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - - -Calculate a statistical test for a numerical variable. — test_cont • DescrTab2 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Calculate a statistical test for a numerical variable.

    - Source: R/descr.R - -
    - -
    -

    Calculate a statistical test for a numerical variable.

    -
    - - 
    test_cont(
-  var,
-  group = NULL,
-  test_options = list(),
-  test = NULL,
-  var_name = NULL
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    var

    A variable (a vector).

    group

    A variable containing the grouping information.

    test_options

    Named list containing test options.

    test

    Name of a statistical test.

    var_name

    Name of variable to be tested (only used in warning messages).

    - -

    Value

    - -

    A list of test test results.

    - -

    Examples

    - 
    cont_var <- c(1, 2, 3)
-test_cont(cont_var)
-
    #> $p
-#> [1] 0.0741799
-#> 
-#> $test_name
-#> [1] "Student's one-sample t-test"
-#>
    -
    - -
    - - -
    -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz.

    -
    - -
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    Site built with pkgdown 1.6.1.

    -
    - -
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    - - - - - - - - diff --git a/docs/reference/write_in_tmpfile_for_cran.html b/docs/reference/write_in_tmpfile_for_cran.html deleted file mode 100644 index 5818d51..0000000 --- a/docs/reference/write_in_tmpfile_for_cran.html +++ /dev/null @@ -1,109 +0,0 @@ - -Function that returns true in CRAN submission — write_in_tmpfile_for_cran • DescrTab2 - - -
    -
    - - - -
    -
    -
    -

    Function that returns true in CRAN submission

    - Source: R/misc.R - -
    - -
    -

    Function that returns true in CRAN submission

    -
    - -
    - 
    write_in_tmpfile_for_cran()
    -
    - -
    -

    Value

    -

    TRUE for CRAN submission, FALSE otherwise

    -
    - -
    - -
    - - -
    -

    Developed by Jan Meis, Lukas Baumann, Maximilian Pilz, Lukas Sauer.

    -
    - -
    -

    Site built with pkgdown 2.0.2.

    -
    - -
    - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index 2467b4c..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,147 +0,0 @@ - - - - https://imbi-heidelberg.github.io/DescrTab2/404.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/a_usage_guide.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/c_other_software_comparison.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/d_validation_statement.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/e_maintenance_guide.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/index.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/maintenance_guide.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/other_software_comparison.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/usage_guide.html - - - https://imbi-heidelberg.github.io/DescrTab2/articles/validation_statement.html - - - https://imbi-heidelberg.github.io/DescrTab2/authors.html - - - https://imbi-heidelberg.github.io/DescrTab2/index.html - - - https://imbi-heidelberg.github.io/DescrTab2/news/index.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/codegen_load_all_sas_data.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/create_character_subtable.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/create_numeric_subtable.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/descr.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/DescrTab2.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/dot-onLoad.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/escape_latex_symbols.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/extract_labels.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/farrington.manning.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/format_freqs.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/guess_ID_variable.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/ignore_unused_args.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/index.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/inMinipage.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/inMinipage2.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/in_minipage.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/knit_print.DescrList.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/knit_print.DescrPrint.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/lapply_descr.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/list_freetext_markdown.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/n_int_digits.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/parse_formats.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/print.DescrList.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/print.DescrPrint.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/print_test_names.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/read_redcap_formatted.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/read_sas_formatted.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/sigfig.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/sigfig_gen.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/sig_test.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/split_redcap_dataset.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/test_cat.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/test_cont.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/unlabel.html - - - https://imbi-heidelberg.github.io/DescrTab2/reference/write_in_tmpfile_for_cran.html - - diff --git a/vignettes/b_test_choice_tree_pdf.Rmd b/vignettes/b_test_choice_tree_pdf.Rmd index 891a139..17fcc46 100644 --- a/vignettes/b_test_choice_tree_pdf.Rmd +++ b/vignettes/b_test_choice_tree_pdf.Rmd @@ -9,7 +9,7 @@ pkgdown: extension: pdf title: Test choice tree -output: html_document +output: pdf_document vignette: > %\VignetteIndexEntry{Test choice tree}