diff --git a/docs/404.html b/docs/404.html index 774ad32..d45caa6 100644 --- a/docs/404.html +++ b/docs/404.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1 diff --git a/docs/CODE_OF_CONDUCT.html b/docs/CODE_OF_CONDUCT.html index 6df8225..6d7631b 100644 --- a/docs/CODE_OF_CONDUCT.html +++ b/docs/CODE_OF_CONDUCT.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1 diff --git a/docs/CONTRIBUTING.html b/docs/CONTRIBUTING.html index 6994540..e66189c 100644 --- a/docs/CONTRIBUTING.html +++ b/docs/CONTRIBUTING.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1 diff --git a/docs/LICENSE.html b/docs/LICENSE.html index 3ddb524..4870d13 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1 diff --git a/docs/articles/custom-plot.html b/docs/articles/custom-plot.html index 6f56cfd..7f5b001 100644 --- a/docs/articles/custom-plot.html +++ b/docs/articles/custom-plot.html @@ -31,7 +31,7 @@ specr - 0.2.0 + 0.2.1 @@ -101,7 +101,7 @@

Customizing plots

-

This vignette exemplifies different ways to plot the specification curve. For most casces, the function plot_specs() takes care of the overall process. However, more specific customizations are possible if we use the more specific functions plot_curve() and plot_choices. Furthermore, we can extend the overall plot with the additional function plot_samplesizes(). All of these functions produce objects of the class ggplot that can be customized further using the grammar of graphics provided by the package ggplot2.

+

This vignette exemplifies different ways to plot the specification curve. For most cases, the function plot_specs() takes care of the overall process. However, more specific customization is possible if we use the more specific functions plot_curve() and plot_choices. Furthermore, we can extend the overall plot with the additional function plot_samplesizes(). All of these functions produce objects of the class ggplot that can be customized further using the grammar of graphics provided by the package ggplot2.

1. Run the specification curve analysis

diff --git a/docs/articles/decompose_var.html b/docs/articles/decompose_var.html index 56224e0..2f82481 100644 --- a/docs/articles/decompose_var.html +++ b/docs/articles/decompose_var.html @@ -31,7 +31,7 @@ specr - 0.2.0 + 0.2.1
@@ -173,7 +173,7 @@

4. Plot variance components

-

Second, we can alternatively use the function plot_variance() to obtain a visualization. The function calls icc_specs() automatically. We can hence pass the multilevel results object directly. Further customizations via the ggplot2 is possible.

+

Second, we can alternatively use the function plot_variance() to obtain a visualization. The function calls icc_specs() automatically. We can hence pass the multilevel results object directly. Further customization via ggplot2 is possible.

plot_variance(m1) +
   ylim(0, 100)

diff --git a/docs/articles/index.html b/docs/articles/index.html index 78f3f0b..2857bcb 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1
diff --git a/docs/articles/invest-spec.html b/docs/articles/invest-spec.html index a7d0440..0dc2758 100644 --- a/docs/articles/invest-spec.html +++ b/docs/articles/invest-spec.html @@ -31,7 +31,7 @@ specr - 0.2.0 + 0.2.1 diff --git a/docs/articles/progress.html b/docs/articles/progress.html index 2d34b5a..d231121 100644 --- a/docs/articles/progress.html +++ b/docs/articles/progress.html @@ -31,7 +31,7 @@ specr - 0.2.0 + 0.2.1 @@ -136,7 +136,7 @@

controls = c("c1", "c2"), subset = list(group1 = unique(example_data$group1), group2 = unique(example_data$group2))) -

The console will show a progress bar during estimation. For more information and additional customizations of the progress bar, see the documentation of the package progress.

+

The console will show a progress bar during estimation. For more information, see the documentation of the package progress.

[==========================================================-------------]  81% (156/192) Finished in 1s
diff --git a/docs/articles/specr.html b/docs/articles/specr.html index 0ada7df..9c47217 100644 --- a/docs/articles/specr.html +++ b/docs/articles/specr.html @@ -31,7 +31,7 @@ specr - 0.2.0 + 0.2.1 @@ -135,7 +135,7 @@

#> Mean : 23.89 Mean : 11.200 Mean :0.5 #> 3rd Qu.: 34.04 3rd Qu.: 17.966 3rd Qu.:1.0 #> Max. : 94.13 Max. : 46.341 Max. :1.0 -

There are several numeric variables. In this exampkle, we assume that x represents independent variables, y represents dependent variables, c represents control variables, and group denotes potential grouping variables that can be used for subsetting the data.

+

There are several numeric variables. In this example, we assume that x represents independent variables, y represents dependent variables, c represents control variables, and group denotes potential grouping variables that can be used for subsetting the data.

@@ -246,7 +246,7 @@

#> 11 group2 = B 2.87e-5 4.25e-5 2.05e-28 0.231 1.13e-15 8.32e-3 162 #> 12 group2 = C 3.03e-7 4.49e-7 2.45e-35 0.487 1.07e-15 3.48e-2 194

The output contains summary statistics such as the median, the median absolute deviation, … as well as the number of observations that were used for each model. Bear in mind that due to subsetting or missing data, sample sizes can vary considerably which, in turn, affects the results (e.g., the p-value).

-

However, in order to grasp how the different analytical choices affect the outcome of interest (in this case, the estimate refers to the unstandarized regression coefficient b), it is reasonable to plot a specification curve. The function plot_specs() to produces the typical visualization of the specification curve and how the analytical choices affected the obtained results.

+

However, in order to grasp how the different analytical choices affect the outcome of interest (in this case, the estimate refers to the unstandardized regression coefficient b), it is reasonable to plot a specification curve. The function plot_specs() to produces the typical visualization of the specification curve and how the analytical choices affected the obtained results.

# Plot specification curve analysis
 plot_specs(results)

diff --git a/docs/articles/specr_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/specr_files/figure-html/unnamed-chunk-8-1.png index 4e74ac9..8b382ed 100644 Binary files a/docs/articles/specr_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/specr_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/authors.html b/docs/authors.html index 27507ad..343baf3 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -67,7 +67,7 @@ specr - 0.2.0 + 0.2.1 @@ -132,11 +132,11 @@

Citation

Masur P, Scharkow M (2019). -“specr: Statistical functions for conducting specification curve analyses (Version 0.2.0).” +“specr: Statistical functions for conducting specification curve analyses (Version 0.2.1).” https://github.com/masurp/specr. 

@Misc{,
-  title = {specr: Statistical functions for conducting specification curve analyses (Version 0.2.0)},
+  title = {specr: Statistical functions for conducting specification curve analyses (Version 0.2.1)},
   author = {Philipp K. Masur and Michael Scharkow},
   year = {2019},
   url = {https://github.com/masurp/specr},
diff --git a/docs/index.html b/docs/index.html
index 24e990f..831de5f 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -31,7 +31,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
@@ -114,13 +114,13 @@ 

 
  • 
 Decomposing the variance of the specification curve: An example of how to investigate variance components of the specification curve.
    • 
 
  • 
-Visualizing progress during estimation: This vignette explains how to create a customizable progress bar for longer computations.
    • 
+Visualizing progress during estimation: This vignette explains how to create a progress bar for longer computations.
 
 
 
    
 
    
 Disclaimer
    
-
    We do see a lot of value in investigating how analytical choices affect a statistical outcome of interest. However, we strongly caution against using specr as a tool to somehow arrive at a better estimate. Running a specification curve analysis does not make your findings any more reliable, valid or generalizable than a single analyis. The method is only meant to inform about the effects of analytical choices on results, and not a better way to estimate a correlation or effect.
    
+
    We do see a lot of value in investigating how analytical choices affect a statistical outcome of interest. However, we strongly caution against using specr as a tool to somehow arrive at a better estimate. Running a specification curve analysis does not make your findings any more reliable, valid or generalizable than a single analysis. The method is only meant to inform about the effects of analytical choices on results, and not a better way to estimate a correlation or effect.
    
 
    
 
    
 
    
@@ -174,7 +174,7 @@ 
    
 #> A BibTeX entry for LaTeX users is
 #> 
 #>   @Misc{,
-#>     title = {specr: Statistical functions for conducting specification curve analyses (Version 0.1.1)},
+#>     title = {specr: Statistical functions for conducting specification curve analyses (Version 0.2.0)},
 #>     author = {Philipp K. Masur and Michael Scharkow},
 #>     year = {2019},
 #>     url = {https://github.com/masurp/specr},
@@ -183,7 +183,7 @@ 
    
 
    
 
    
 References
    
-
    Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2019). Specification Curve: Descriptive and Inferential Statistics for all Plausible Specifications. Available at: http://dx.doi.org/10.2139/ssrn.2694998
    
+
    Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2019). Specification Curve: Descriptive and Inferential Statistics for all Plausible Specifications. Available at: https://doi.org/10.2139/ssrn.2694998
    
 
    Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. Perspectives on Psychological Science, 11(5), 702-712. https://doi.org/10.1177/1745691616658637
    
 
    
 
    
diff --git a/docs/reference/example_data.html b/docs/reference/example_data.html
index aa20d49..d0d7f5b 100644
--- a/docs/reference/example_data.html
+++ b/docs/reference/example_data.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/icc_specs.html b/docs/reference/icc_specs.html
index 01e5ef1..0ae97ba 100644
--- a/docs/reference/icc_specs.html
+++ b/docs/reference/icc_specs.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
@@ -149,7 +149,7 @@ 
    Arg
     
     
       percent
-      
    a logical value indicating whether the icc should also be printed as percentage. Defaults to TRUE.
    
+      
    a logical value indicating whether the ICC should also be printed as percentage. Defaults to TRUE.
    
     
     
 
@@ -160,7 +160,7 @@ 
    R
 
     
 
 
     
    See also
    
diff --git a/docs/reference/index.html b/docs/reference/index.html
index cb18e64..6407505 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -67,7 +67,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html
index a0be185..bbd4950 100644
--- a/docs/reference/pipe.html
+++ b/docs/reference/pipe.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_choices-1.png b/docs/reference/plot_choices-1.png
index d50780c..211a706 100644
Binary files a/docs/reference/plot_choices-1.png and b/docs/reference/plot_choices-1.png differ
diff --git a/docs/reference/plot_choices-2.png b/docs/reference/plot_choices-2.png
index 8e5bada..60bce74 100644
Binary files a/docs/reference/plot_choices-2.png and b/docs/reference/plot_choices-2.png differ
diff --git a/docs/reference/plot_choices.html b/docs/reference/plot_choices.html
index 490b0ea..9e10aa4 100644
--- a/docs/reference/plot_choices.html
+++ b/docs/reference/plot_choices.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_curve-1.png b/docs/reference/plot_curve-1.png
index 4918345..91dc8f6 100644
Binary files a/docs/reference/plot_curve-1.png and b/docs/reference/plot_curve-1.png differ
diff --git a/docs/reference/plot_curve-2.png b/docs/reference/plot_curve-2.png
index 2e20c25..d289fa2 100644
Binary files a/docs/reference/plot_curve-2.png and b/docs/reference/plot_curve-2.png differ
diff --git a/docs/reference/plot_curve.html b/docs/reference/plot_curve.html
index bc7ba38..41f5f01 100644
--- a/docs/reference/plot_curve.html
+++ b/docs/reference/plot_curve.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_decisiontree-3.png b/docs/reference/plot_decisiontree-3.png
index 110c239..61e4836 100644
Binary files a/docs/reference/plot_decisiontree-3.png and b/docs/reference/plot_decisiontree-3.png differ
diff --git a/docs/reference/plot_decisiontree.html b/docs/reference/plot_decisiontree.html
index 82ef6d1..6ea8bda 100644
--- a/docs/reference/plot_decisiontree.html
+++ b/docs/reference/plot_decisiontree.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_samplesizes-1.png b/docs/reference/plot_samplesizes-1.png
index f18ea09..f8a12ee 100644
Binary files a/docs/reference/plot_samplesizes-1.png and b/docs/reference/plot_samplesizes-1.png differ
diff --git a/docs/reference/plot_samplesizes-2.png b/docs/reference/plot_samplesizes-2.png
index 8881871..c7404dc 100644
Binary files a/docs/reference/plot_samplesizes-2.png and b/docs/reference/plot_samplesizes-2.png differ
diff --git a/docs/reference/plot_samplesizes.html b/docs/reference/plot_samplesizes.html
index 933bbf6..37ffd87 100644
--- a/docs/reference/plot_samplesizes.html
+++ b/docs/reference/plot_samplesizes.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_specs.html b/docs/reference/plot_specs.html
index 97160cd..ae4a379 100644
--- a/docs/reference/plot_specs.html
+++ b/docs/reference/plot_specs.html
@@ -70,7 +70,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_summary-1.png b/docs/reference/plot_summary-1.png
index 1e6dd58..257d6e6 100644
Binary files a/docs/reference/plot_summary-1.png and b/docs/reference/plot_summary-1.png differ
diff --git a/docs/reference/plot_summary.html b/docs/reference/plot_summary.html
index 63103bb..af83316 100644
--- a/docs/reference/plot_summary.html
+++ b/docs/reference/plot_summary.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
diff --git a/docs/reference/plot_variance-1.png b/docs/reference/plot_variance-1.png
index 5c539ab..926b0d6 100644
Binary files a/docs/reference/plot_variance-1.png and b/docs/reference/plot_variance-1.png differ
diff --git a/docs/reference/plot_variance.html b/docs/reference/plot_variance.html
index 65e3cac..c9fe0e9 100644
--- a/docs/reference/plot_variance.html
+++ b/docs/reference/plot_variance.html
@@ -36,7 +36,7 @@
 
 
 
-
+
 
 
 
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
 
@@ -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).
    
+    
    This functions creates a simple barplot that visually displays how much variance in the outcome (e.g., the regression coefficient) 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)
    
@@ -154,7 +154,7 @@ 
    Value
    
     
    a ggplot object.
    
     
    See also
    
 
-    
    icc_specs() to produce a tibble that details the variance decomposion.
    
+    
    icc_specs() to produce a tibble that details the variance decomposition.
    
 
     
    Examples
    
     
    # Step 1: Run spec curve analysis
diff --git a/docs/reference/run_specs.html b/docs/reference/run_specs.html
index fc8d61b..b9122bd 100644
--- a/docs/reference/run_specs.html
+++ b/docs/reference/run_specs.html
@@ -69,7 +69,7 @@
       
       
         specr
-        0.2.0
+        0.2.1
       
     
    
 
@@ -193,7 +193,7 @@ 
    R
 
     
 
      
-
    • Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2019). Specification Curve: Descriptive and Inferential Statistics for all Plausible Specifications. Available at: http://dx.doi.org/10.2139/ssrn.2694998
      • 
+
    • Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2019). Specification Curve: Descriptive and Inferential Statistics for all Plausible Specifications. Available at: https://doi.org/10.2139/ssrn.2694998
      • 
 
    • Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. Perspectives on Psychological Science, 11(5), 702-712. https://doi.org/10.1177/1745691616658637
      • 
 
    
 
@@ -212,21 +212,21 @@ 
    Examp
                                     group2 = unique(example_data$group2)))
 
 # Check results frame
-results
    #> # A tibble: 192 x 12
+results
    #> # A tibble: 192 x 12
 #>    x     y     model controls estimate std.error statistic  p.value conf.low
-#>    <chr> <chr> <chr> <chr>       <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
-#>  1 x1    y1    lm    c1 + c2    4.95       0.525    9.43   3.11e-18    3.92 
-#>  2 x2    y1    lm    c1 + c2    6.83       0.321   21.3    1.20e-57    6.20 
-#>  3 x1    y2    lm    c1 + c2   -0.227      0.373   -0.607  5.44e- 1   -0.961
-#>  4 x2    y2    lm    c1 + c2    0.985      0.324    3.04   2.62e- 3    0.347
-#>  5 x1    y1    lm    c1         5.53       0.794    6.97   2.95e-11    3.96 
-#>  6 x2    y1    lm    c1         8.07       0.557   14.5    6.90e-35    6.98 
-#>  7 x1    y2    lm    c1         0.0461     0.466    0.0989 9.21e- 1   -0.872
-#>  8 x2    y2    lm    c1         1.61       0.394    4.10   5.72e- 5    0.837
-#>  9 x1    y1    lm    c2         5.15       0.625    8.24   9.95e-15    3.92 
-#> 10 x2    y1    lm    c2         6.50       0.466   13.9    5.38e-33    5.58 
-#> # … with 182 more rows, and 3 more variables: conf.high <dbl>, obs <int>,
-#> #   subsets <chr>
    
+#>    <chr> <chr> <chr> <chr>       <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
+#>  1 x1    y1    lm    c1 + c2    4.95       0.525    9.43   3.11e-18    3.92 
+#>  2 x2    y1    lm    c1 + c2    6.83       0.321   21.3    1.20e-57    6.20 
+#>  3 x1    y2    lm    c1 + c2   -0.227      0.373   -0.607  5.44e- 1   -0.961
+#>  4 x2    y2    lm    c1 + c2    0.985      0.324    3.04   2.62e- 3    0.347
+#>  5 x1    y1    lm    c1         5.53       0.794    6.97   2.95e-11    3.96 
+#>  6 x2    y1    lm    c1         8.07       0.557   14.5    6.90e-35    6.98 
+#>  7 x1    y2    lm    c1         0.0461     0.466    0.0989 9.21e- 1   -0.872
+#>  8 x2    y2    lm    c1         1.61       0.394    4.10   5.72e- 5    0.837
+#>  9 x1    y1    lm    c2         5.15       0.625    8.24   9.95e-15    3.92 
+#> 10 x2    y1    lm    c2         6.50       0.466   13.9    5.38e-33    5.58 
+#> # … with 182 more rows, and 3 more variables: conf.high <dbl>, obs <int>,
+#> #   subsets <chr>
    
 
    
   
   
 
@@ -172,17 +172,17 @@ 
    Examp
     
    setup_specs(y = c("y1"),
             x = c("x1", "x2"),
             model = c("lm"),
-            controls = c("c1", "c2"))
    #> # A tibble: 8 x 4
+            controls = c("c1", "c2"))
    #> # A tibble: 8 x 4
 #>   x     y     model controls     
-#>   <chr> <chr> <chr> <chr>        
-#> 1 x1    y1    lm    c1 + c2      
-#> 2 x2    y1    lm    c1 + c2      
-#> 3 x1    y1    lm    c1           
-#> 4 x2    y1    lm    c1           
-#> 5 x1    y1    lm    c2           
-#> 6 x2    y1    lm    c2           
-#> 7 x1    y1    lm    no covariates
-#> 8 x2    y1    lm    no covariates
    
+#>   <chr> <chr> <chr> <chr>        
+#> 1 x1    y1    lm    c1 + c2      
+#> 2 x2    y1    lm    c1 + c2      
+#> 3 x1    y1    lm    c1           
+#> 4 x2    y1    lm    c1           
+#> 5 x1    y1    lm    c2           
+#> 6 x2    y1    lm    c2           
+#> 7 x1    y1    lm    no covariates
+#> 8 x2    y1    lm    no covariates
    
 
    
   
   
 
@@ -185,51 +185,51 @@ 
    Examp
                                     group2 = unique(example_data$group2)))
 
 # overall summary
-summarise_specs(results)
    #> # A tibble: 1 x 7
+summarise_specs(results)
    #> # A tibble: 1 x 7
 #>   median   mad   min   max   q25   q75   obs
-#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1   3.59  4.56 -2.05  9.58  1.03  7.63   123
    
+#>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
+#> 1   3.59  4.56 -2.05  9.58  1.03  7.63   123
    
 # Summary of specific analytical choices
 summarise_specs(results,    # data frame
-                x, y)       # analytical choices
    #> # A tibble: 4 x 9
-#> # Groups:   x [2]
+                x, y)       # analytical choices
    #> # A tibble: 4 x 9
+#> # Groups:   x [2]
 #>   x     y     median   mad    min   max    q25   q75   obs
-#>   <chr> <chr>  <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl>
-#> 1 x1    y1     6.52  2.01   3.49   9.28  5.46   8.22   123
-#> 2 x1    y2     0.498 1.96  -2.05   3.67 -0.568  1.60   123
-#> 3 x2    y1     7.80  0.669  5.89   9.58  7.33   8.21   123
-#> 4 x2    y2     1.29  0.799 -0.258  2.91  0.823  1.80   123
    
+#>   <chr> <chr>  <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl>
+#> 1 x1    y1     6.52  2.01   3.49   9.28  5.46   8.22   123
+#> 2 x1    y2     0.498 1.96  -2.05   3.67 -0.568  1.60   123
+#> 3 x2    y1     7.80  0.669  5.89   9.58  7.33   8.21   123
+#> 4 x2    y2     1.29  0.799 -0.258  2.91  0.823  1.80   123
    
 # Summary of other parameters across several analytical choices
 summarise_specs(results,
                 subsets, controls,
                 var = p.value,
                 stats = list(median = median,
                              min = min,
-                             max = max))
    #> # A tibble: 48 x 6
-#> # Groups:   subsets [12]
+                             max = max))
    #> # A tibble: 48 x 6
+#> # Groups:   subsets [12]
 #>    subsets                 controls          median      min     max   obs
-#>    <chr>                   <chr>              <dbl>    <dbl>   <dbl> <dbl>
-#>  1 all                     c1            0.00000536 9.94e-47 0.792     500
-#>  2 all                     c1 + c2       0.00000184 5.08e-89 0.838     500
-#>  3 all                     c2            0.0000559  8.09e-63 0.377     500
-#>  4 all                     no covariates 0.0000309  8.62e-40 0.252     500
-#>  5 group1 = 0              c1            0.0000286  6.90e-35 0.921     250
-#>  6 group1 = 0              c1 + c2       0.00131    1.20e-57 0.544     250
-#>  7 group1 = 0              c2            0.0817     5.38e-33 0.994     250
-#>  8 group1 = 0              no covariates 0.00528    2.57e-24 0.568     250
-#>  9 group1 = 0 & group2 = A c1            0.00258    3.15e- 8 0.0235     72
-#> 10 group1 = 0 & group2 = A c1 + c2       0.000222   6.98e-16 0.00253    72
-#> # … with 38 more rows
    
+#>    <chr>                   <chr>              <dbl>    <dbl>   <dbl> <dbl>
+#>  1 all                     c1            0.00000536 9.94e-47 0.792     500
+#>  2 all                     c1 + c2       0.00000184 5.08e-89 0.838     500
+#>  3 all                     c2            0.0000559  8.09e-63 0.377     500
+#>  4 all                     no covariates 0.0000309  8.62e-40 0.252     500
+#>  5 group1 = 0              c1            0.0000286  6.90e-35 0.921     250
+#>  6 group1 = 0              c1 + c2       0.00131    1.20e-57 0.544     250
+#>  7 group1 = 0              c2            0.0817     5.38e-33 0.994     250
+#>  8 group1 = 0              no covariates 0.00528    2.57e-24 0.568     250
+#>  9 group1 = 0 & group2 = A c1            0.00258    3.15e- 8 0.0235     72
+#> 10 group1 = 0 & group2 = A c1 + c2       0.000222   6.98e-16 0.00253    72
+#> # … with 38 more rows
    
 # Unnamed vector instead of named list passed to `stats`
 summarise_specs(results,
                 controls,
-                stats = c(mean, median))
    #> # A tibble: 4 x 4
+                stats = c(mean, median))
    #> # A tibble: 4 x 4
 #>   controls        fn1   fn2   obs
-#>   <chr>         <dbl> <dbl> <dbl>
-#> 1 c1             4.07  3.16   123
-#> 2 c1 + c2        3.79  3.16   123
-#> 3 c2             4.08  3.79   123
-#> 4 no covariates  4.38  3.95   123
    
+#>   <chr>         <dbl> <dbl> <dbl>
+#> 1 c1             4.07  3.16   123
+#> 2 c1 + c2        3.79  3.16   123
+#> 3 c2             4.08  3.79   123
+#> 4 no covariates  4.38  3.95   123