From 5838419302683e3a2f3d1cff8c040cb9bb8b0218 Mon Sep 17 00:00:00 2001 From: Benkeser Date: Thu, 20 May 2021 16:31:03 -0400 Subject: [PATCH] decrease default bootstrap to make examples run faster --- R/cdf_estimation.R | 2 +- README-unnamed-chunk-3-1.png | Bin 23185 -> 23181 bytes README-unnamed-chunk-4-1.png | Bin 23846 -> 23876 bytes docs/articles/using_drord.html | 22 +++++++++--------- .../figure-html/cdf-plot-1.png | Bin 27906 -> 27907 bytes .../figure-html/pmf-plot-1.png | Bin 30581 -> 30559 bytes .../figure-html/pmf-plot-with-text-1.png | Bin 35939 -> 35917 bytes docs/pkgdown.yml | 2 +- docs/reference/drord.html | 2 +- man/drord.Rd | 2 +- vignettes/using_drord.Rmd | 2 +- 11 files changed, 16 insertions(+), 16 deletions(-) diff --git a/R/cdf_estimation.R b/R/cdf_estimation.R index 21e9630..0856515 100644 --- a/R/cdf_estimation.R +++ b/R/cdf_estimation.R @@ -136,7 +136,7 @@ drord <- function( param = c("weighted_mean", "log_odds", "mann_whitney"), ci = "wald", alpha = 0.05, - 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Bootstrap confidence intervals

-

By default, drord will used closed-form inference. While this is generally faster, we may instead prefer to use bootstrap confidence intervals, which should provide more robust behavior when working models are misspecified. Bootstrap intervals can be requested via the ci = "bca", with option nboot determining the number of bootstrap resamples performed. The bootstrap employed by the package is the bias corrected and accelerated bootstrap (Efron and Tibshirani 1994). In practice, we recommend using many bootstrap samples (e.g., the package default, nboot = 1e4), so that inference is not dependent on the random seed that is set; however, here we illustrate using a small number of bootstrap samples for fast compilation of this vignette. To increase speed, we also turn off estimation of confidence intervals for the CDF and PMF by setting est_dist = FALSE.

+

By default, drord will used closed-form inference. While this is generally faster, we may instead prefer to use bootstrap confidence intervals, which should provide more robust behavior when working models are misspecified. Bootstrap intervals can be requested via the ci = "bca", with option nboot determining the number of bootstrap resamples performed. The bootstrap employed by the package is the bias corrected and accelerated bootstrap (Efron and Tibshirani 1994). In practice, we recommend using many bootstrap samples (e.g., nboot = 1e4), so that inference is not dependent on the random seed that is set; however, here we illustrate using a small number of bootstrap samples for fast compilation of this vignette. To increase speed, we also turn off estimation of confidence intervals for the CDF and PMF by setting est_dist = FALSE.

 (fit4 <- drord(out = covid19$out, treat = covid19$treat, 
                covar = covid19[ , "age_grp", drop = FALSE],
@@ -848,7 +848,7 @@ 

est_dist = FALSE)) # save time by not computing CIs for CDF/PMF

## $mann_whitney
 ##       est   bca_cil   bca_ciu 
-## 0.5800689 0.5562334 0.6331968
+## 0.5800689 0.5471615 0.6146912

@@ -910,19 +910,19 @@

treat_form = "I(age_grp < 5)"))

## $mann_whitney
 ##       est  wald_cil  wald_ciu 
-## 0.5746986 0.5242685 0.6251286 
+## 0.5612776 0.5122419 0.6103134 
 ## 
 ## $log_odds
-##               est   wald_cil    wald_ciu
-## treat1 -1.2635719 -1.5570427 -0.97010110
-## treat0 -0.9481107 -1.2082170 -0.68800435
-## diff   -0.3154613 -0.6924009  0.06147834
+##               est   wald_cil   wald_ciu
+## treat1 -1.1955037 -1.4844206 -0.9065868
+## treat0 -1.0008050 -1.2638434 -0.7377667
+## diff   -0.1946987 -0.5698264  0.1804291
 ## 
 ## $weighted_mean
-##             est   wald_cil  wald_ciu
-## treat1 2.545048 2.44660010 2.6434965
-## treat0 2.380465 2.28901944 2.4719112
-## diff   0.164583 0.03523862 0.2939274
+## est wald_cil wald_ciu +## treat1 2.5249679 2.423789789 2.626146 +## treat0 2.4025430 2.311798726 2.493287 +## diff 0.1224249 -0.008130262 0.252980

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Doubly robust estimates of for evaluating effects param = c("weighted_mean", "log_odds", "mann_whitney"), ci = "wald", alpha = 0.05, - nboot = 10000, + nboot = 1000, return_models = TRUE, est_dist = TRUE, stratify = FALSE, diff --git a/man/drord.Rd b/man/drord.Rd index 8156ed6..94e7e8f 100644 --- a/man/drord.Rd +++ b/man/drord.Rd @@ -17,7 +17,7 @@ drord( param = c("weighted_mean", "log_odds", "mann_whitney"), ci = "wald", alpha = 0.05, - nboot = 10000, + nboot = 1000, return_models = TRUE, est_dist = TRUE, stratify = FALSE, diff --git a/vignettes/using_drord.Rmd b/vignettes/using_drord.Rmd index 43bd547..019f337 100644 --- a/vignettes/using_drord.Rmd +++ b/vignettes/using_drord.Rmd @@ -216,7 +216,7 @@ fit3$out_mod$treat1 ## Bootstrap confidence intervals -By default, `drord` will used closed-form inference. While this is generally faster, we may instead prefer to use bootstrap confidence intervals, which should provide more robust behavior when working models are misspecified. Bootstrap intervals can be requested via the `ci = "bca"`, with option `nboot` determining the number of bootstrap resamples performed. The bootstrap employed by the package is the bias corrected and accelerated bootstrap [@efron1994introduction]. **In practice, we recommend using many bootstrap samples** (e.g., the package default, `nboot = 1e4`), so that inference is not dependent on the random seed that is set; however, here we illustrate using a small number of bootstrap samples for fast compilation of this vignette. To increase speed, we also turn off estimation of confidence intervals for the CDF and PMF by setting `est_dist = FALSE`. +By default, `drord` will used closed-form inference. While this is generally faster, we may instead prefer to use bootstrap confidence intervals, which should provide more robust behavior when working models are misspecified. Bootstrap intervals can be requested via the `ci = "bca"`, with option `nboot` determining the number of bootstrap resamples performed. The bootstrap employed by the package is the bias corrected and accelerated bootstrap [@efron1994introduction]. **In practice, we recommend using many bootstrap samples** (e.g., `nboot = 1e4`), so that inference is not dependent on the random seed that is set; however, here we illustrate using a small number of bootstrap samples for fast compilation of this vignette. To increase speed, we also turn off estimation of confidence intervals for the CDF and PMF by setting `est_dist = FALSE`. ```{r} (fit4 <- drord(out = covid19$out, treat = covid19$treat,