oneway-anova-nonparametric.md
In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

between-subjects

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 14
    parameter1 parameter2 statistic df.error      p.value
    <chr>      <chr>          <dbl>    <int>        <dbl>
  1 length     genre           51.4        8 0.0000000217
    method                       effectsize      estimate conf.level conf.low
    <chr>                        <chr>              <dbl>      <dbl>    <dbl>
  1 Kruskal-Wallis rank sum test Epsilon2 (rank)    0.328       0.95    0.258
    conf.high conf.method          conf.iterations n.obs
        <dbl> <chr>                          <int> <int>
  1         1 percentile bootstrap             100   158

Code
  df1[["expression"]]
Output
  [[1]]
  list(chi["Kruskal-Wallis"]^2 * "(" * 8 * ")" == "51.43", italic(p) == 
      "2.17e-08", widehat(epsilon)["ordinal"]^2 == "0.33", CI["95%"] ~ 
      "[" * "0.26", "1.00" * "]", italic("n")["obs"] == "158")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 14
    parameter1  parameter2 statistic df.error p.value method                      
    <chr>       <chr>          <dbl>    <int>   <dbl> <chr>                       
  1 sleep_total vore            3.30        3   0.348 Kruskal-Wallis rank sum test
    effectsize      estimate conf.level conf.low conf.high conf.method         
    <chr>              <dbl>      <dbl>    <dbl>     <dbl> <chr>               
  1 Epsilon2 (rank)   0.0440       0.99  0.00729         1 percentile bootstrap
    conf.iterations n.obs
              <int> <int>
  1             100    76

Code
  df2[["expression"]]
Output
  [[1]]
  list(chi["Kruskal-Wallis"]^2 * "(" * 3 * ")" == "3.30", italic(p) == 
      "0.35", widehat(epsilon)["ordinal"]^2 == "0.04", CI["99%"] ~ 
      "[" * "7.29e-03", "1.00" * "]", italic("n")["obs"] == "76")

within-subjects

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 14
    parameter1 parameter2 statistic df.error  p.value method                
    <chr>      <chr>          <dbl>    <dbl>    <dbl> <chr>                 
  1 desire     condition       55.8        3 4.56e-12 Friedman rank sum test
    effectsize  estimate conf.level conf.low conf.high conf.method         
    <chr>          <dbl>      <dbl>    <dbl>     <dbl> <chr>               
  1 Kendall's W    0.211       0.99    0.140         1 percentile bootstrap
    conf.iterations n.obs
              <int> <int>
  1             100    88

Code
  df1[["expression"]]
Output
  [[1]]
  list(chi["Friedman"]^2 * "(" * 3 * ")" == "55.83", italic(p) == 
      "4.56e-12", widehat(italic("W"))["Kendall"] == "0.21", CI["99%"] ~ 
      "[" * "0.14", "1.00" * "]", italic("n")["pairs"] == "88")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 14
    parameter1 parameter2 statistic df.error  p.value method                
    <chr>      <chr>          <dbl>    <dbl>    <dbl> <chr>                 
  1 value      condition        410        3 1.51e-88 Friedman rank sum test
    effectsize  estimate conf.level conf.low conf.high conf.method         
    <chr>          <dbl>      <dbl>    <dbl>     <dbl> <chr>               
  1 Kendall's W    0.911        0.9    0.906         1 percentile bootstrap
    conf.iterations n.obs
              <int> <int>
  1             100   150

Code
  df2[["expression"]]
Output
  [[1]]
  list(chi["Friedman"]^2 * "(" * 3 * ")" == "410.00", italic(p) == 
      "1.51e-88", widehat(italic("W"))["Kendall"] == "0.91", CI["90%"] ~ 
      "[" * "0.91", "1.00" * "]", italic("n")["pairs"] == "150")