oneway_anova_robust.md
In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

expr_anova_robust works - between-subjects

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 11
    statistic    df df.error   p.value
        <dbl> <dbl>    <dbl>     <dbl>
  1      20.2     2     19.0 0.0000196
    method                                           
    <chr>                                            
  1 A heteroscedastic one-way ANOVA for trimmed means
    effectsize                         estimate conf.level conf.low conf.high
    <chr>                                 <dbl>      <dbl>    <dbl>     <dbl>
  1 Explanatory measure of effect size    0.859       0.95    0.853     0.864
    n.obs
    <int>
  1    32

Code
  df1[["expression"]]
Output
  [[1]]
  list(italic("F")["trimmed-means"](2, 18.97383) == "20.24946", 
      italic(p) == "0.00002", widehat(xi) == "0.85858", CI["95%"] ~ 
          "[" * "0.85268", "0.86448" * "]", italic("n")["obs"] == 
          "32")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 11
    statistic    df df.error p.value
        <dbl> <dbl>    <dbl>   <dbl>
  1    0.0503     2     21.7   0.951
    method                                           
    <chr>                                            
  1 A heteroscedastic one-way ANOVA for trimmed means
    effectsize                         estimate conf.level conf.low conf.high
    <chr>                                 <dbl>      <dbl>    <dbl>     <dbl>
  1 Explanatory measure of effect size    0.201       0.99   0.0872     0.754
    n.obs
    <int>
  1    71

Code
  df2[["expression"]]
Output
  [[1]]
  list(italic("F")["trimmed-means"](2, 21.6869) == "0.0503", italic(p) == 
      "0.9511", widehat(xi) == "0.2013", CI["99%"] ~ "[" * "0.0872", 
      "0.7537" * "]", italic("n")["obs"] == "71")

expr_anova_robust works - within-subjects

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 11
    statistic    df df.error  p.value
        <dbl> <dbl>    <dbl>    <dbl>
  1      21.0  2.73     145. 1.15e-10
    method                                                             
    <chr>                                                              
  1 A heteroscedastic one-way repeated measures ANOVA for trimmed means
    effectsize                                                      estimate
    <chr>                                                              <dbl>
  1 Algina-Keselman-Penfield robust standardized difference average    0.664
    conf.level conf.low conf.high n.obs
         <dbl>    <dbl>     <dbl> <int>
  1       0.95    0.466     0.971    88

Code
  df1[["expression"]]
Output
  [[1]]
  list(italic("F")["trimmed-means"](2.7303, 144.7051) == "20.9752", 
      italic(p) == "1.1462e-10", widehat(delta)["R-avg"]^"AKP" == 
          "0.6635", CI["95%"] ~ "[" * "0.4660", "0.9707" * "]", 
      italic("n")["pairs"] == "88")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 11
    statistic    df df.error p.value
        <dbl> <dbl>    <dbl>   <dbl>
  1      22.1     1        3  0.0182
    method                                                             
    <chr>                                                              
  1 A heteroscedastic one-way repeated measures ANOVA for trimmed means
    effectsize                                                      estimate
    <chr>                                                              <dbl>
  1 Algina-Keselman-Penfield robust standardized difference average     -Inf
    conf.level conf.low conf.high n.obs
         <dbl>    <dbl>     <dbl> <int>
  1       0.95     -Inf       NaN     4

Code
  df2[["expression"]]
Output
  [[1]]
  list(italic("F")["trimmed-means"](1, 3) == "22.09", italic(p) == 
      "0.02", widehat(delta)["R-avg"]^"AKP" == "-Inf", CI["95%"] ~ 
      "[" * "-Inf", "NA" * "]", italic("n")["pairs"] == "4")