From ecf094d79ac7588bdcfe936ccfa642b1b84e17e2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Adrian=20Du=C8=99a?= Date: Sat, 9 Dec 2023 23:09:04 +0200 Subject: [PATCH] small corrections --- vignettes/b_Added_value.Rmd | 6 +++--- vignettes/c_Weighting.Rmd | 6 ++++-- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/vignettes/b_Added_value.Rmd b/vignettes/b_Added_value.Rmd index 798454e..4c1e289 100644 --- a/vignettes/b_Added_value.Rmd +++ b/vignettes/b_Added_value.Rmd @@ -254,9 +254,9 @@ x6 <- declared( x6 ``` -Either as a character, numeric or categorical, it is possible to declare and use -special types of missing values, employing this new object type of class -`"declared"`{.R}. +Either as a character, numeric, categorical, or even a Date variable, it is +possible to declare and use special types of missing values, employing this new +object type of class `"declared"`{.R}. Factors and `haven`{.R} objects have default coercion methods, but not all types of objects can be automatically coerced to this class. To meet this possibility, diff --git a/vignettes/c_Weighting.Rmd b/vignettes/c_Weighting.Rmd index 6cdb1a4..00aa8ed 100644 --- a/vignettes/c_Weighting.Rmd +++ b/vignettes/c_Weighting.Rmd @@ -86,7 +86,7 @@ family of functions are solely dealing with frequency weights, to allow corrections in descriptive statistics, such as the tables of frequencies and other similar descriptive measures for both categorical and numeric variables. -To exemplify, a frequency weights variable is constructed, to correct for the +To exemplify, a frequency weight variable is constructed, to correct for the distributions of gender by males and females, as well as the theoretical distribution by residential areas differentiating between urban and rural settlements. @@ -97,7 +97,9 @@ op <- with(dfm, proportions(table(Gender, Area))) # Theoretical / population proportions: # 53% Rural, and 50% Females -weights <- rep(c(0.53, 0.47), each = 2) * rep(0.5, 4) / op +tp <- rep(c(0.53, 0.47), each = 2) * rep(0.5, 4) + +weights <- tp / op dfm$fweight <- weights[ match(10 * dfm$Area + dfm$Gender, c(11, 12, 21, 22))