From 6dc33f081ab9f3684f2c85a79632249301e081c9 Mon Sep 17 00:00:00 2001 From: "Philipp K. Masur" Date: Wed, 25 Mar 2020 16:09:46 +0100 Subject: [PATCH] Update website. --- docs/reference/plot_choices.html | 4 ++-- docs/reference/plot_curve.html | 8 ++++---- docs/reference/plot_decisiontree.html | 4 ++-- docs/reference/plot_samplesizes.html | 4 ++-- docs/reference/plot_specs.html | 3 +-- docs/reference/plot_summary.html | 4 ++-- docs/reference/plot_variance.html | 4 ++-- 7 files changed, 15 insertions(+), 16 deletions(-) diff --git a/docs/reference/plot_choices.html b/docs/reference/plot_choices.html index aedd90d..490b0ea 100644 --- a/docs/reference/plot_choices.html +++ b/docs/reference/plot_choices.html @@ -36,7 +36,7 @@ - + @@ -135,7 +135,7 @@

Plot how analytical choices affect results

-

This functions plots how analytical choices affect the obtained results (i.e., the rank within the curve). Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). Further customization using ggplot is possible. This functions creates the lower panel in plot_specs().

+

This functions plots how analytical choices affect the obtained results (i.e., the rank within the curve). Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the lower panel in plot_specs().

plot_choices(
diff --git a/docs/reference/plot_curve.html b/docs/reference/plot_curve.html
index 97b364d..bc7ba38 100644
--- a/docs/reference/plot_curve.html
+++ b/docs/reference/plot_curve.html
@@ -36,7 +36,7 @@
 
 
 
-
+
 
 
 
@@ -135,7 +135,7 @@ 
Plot ranked specification curve

     
 
     

-    
This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). Further customization using ggplot is possible. This functions creates the upper panel in plot_specs().

+    
This function plots the a ranked specification curve. Confidence intervals can be included. Significant results are highlighted (negative = red, positive = blue, grey = nonsignificant). This functions creates the upper panel in plot_specs().

     

 
     
plot_curve(
@@ -182,8 +182,8 @@ 
Value

 
     
Examples

     
# load additional library
-library(ggplot2) # for further customization of the plots
#> Want to understand how all the pieces fit together? Read R for Data
-#> Science: https://r4ds.had.co.nz/

+library(ggplot2) # for further customization of the plots
+
 # Run specification curve analysis
 results <- run_specs(df = example_data,
                      y = c("y1", "y2"),
diff --git a/docs/reference/plot_decisiontree.html b/docs/reference/plot_decisiontree.html
index 85c205e..82ef6d1 100644
--- a/docs/reference/plot_decisiontree.html
+++ b/docs/reference/plot_decisiontree.html
@@ -36,7 +36,7 @@
 
 
 
-
+
 
 
 
@@ -135,7 +135,7 @@ 
Plot decision tree

     

 
     

-    
This function plots a simple decision tree that is meant to help understanding how few analytical choices may results in a large number of specifications. It is somewhat useless if the final number of specifications is very high. Further customization using ggplot is possible.

+    
This function plots a simple decision tree that is meant to help understanding how few analytical choices may results in a large number of specifications. It is somewhat useless if the final number of specifications is very high.

     

 
     
plot_decisiontree(df, label = FALSE, legend = FALSE)

diff --git a/docs/reference/plot_samplesizes.html b/docs/reference/plot_samplesizes.html
index 61bd8a6..933bbf6 100644
--- a/docs/reference/plot_samplesizes.html
+++ b/docs/reference/plot_samplesizes.html
@@ -36,7 +36,7 @@
 
 
 
-
+
 
 
 
@@ -135,7 +135,7 @@ 
Plot sample sizes

     
 
     

-    
This function plots a histogram of sample sizes per specification. It can be added to the overall specification curve plot (see vignettes). Further customization using ggplot is possible.

+    
This function plots a histogram of sample sizes per specification. It can be added to the overall specification curve plot (see vignettes).

     

 
     
plot_samplesizes(df, desc = FALSE)

diff --git a/docs/reference/plot_specs.html b/docs/reference/plot_specs.html
index 7df720c..97160cd 100644
--- a/docs/reference/plot_specs.html
+++ b/docs/reference/plot_specs.html
@@ -254,8 +254,7 @@ 
Examp
 
 plot_specs(plot_a = p1,    # arguments must be called directly!
            plot_b = p2,
-           rel_height = c(2, 2)) %>% class
#> [1] "gg"     "ggplot"

-

+           rel_height = c(2, 2))

   
   
 
     

-    
This function provides a convenient way to visually investigate the effect of individual choices on the estimate of interest. It produces box-and-whisker plot(s) for each provided analytical choice. Further customization using ggplot is possible.

+    
This function provides a convenient way to visually investigate the effect of individual choices on the estimate of interest. It produces box-and-whisker plot(s) for each provided analytical choice.

     

 
     
plot_summary(df, choices = c("x", "y", "model", "controls", "subsets"))

diff --git a/docs/reference/plot_variance.html b/docs/reference/plot_variance.html
index 459e5db..65e3cac 100644
--- a/docs/reference/plot_variance.html
+++ b/docs/reference/plot_variance.html
@@ -36,7 +36,7 @@
 
 
 
-
+
 
 
 
@@ -135,7 +135,7 @@ 
Plot variance decomposition

     
 
     

-    
This functions creates a simple barplot that visually displays how much variance in the outcome (e.g., the regression coeficient) different analytical choices or combinations therefor account for. To use this approach, one needs to estimate a multilevel model that includes all analytical choices as grouping variables (see examples and vignettes). This function uses icc_specs() to compute the intraclass correlation coefficients (ICCs), which provides the data basis for the plot (see examples). Further customization using ggplot is possible.

+    
This functions creates a simple barplot that visually displays how much variance in the outcome (e.g., the regression coeficient) different analytical choices or combinations therefor account for. To use this approach, one needs to estimate a multilevel model that includes all analytical choices as grouping variables (see examples and vignettes). This function uses icc_specs() to compute the intraclass correlation coefficients (ICCs), which provides the data basis for the plot (see examples).

     

 
     
plot_variance(model)