contingency-table.md
In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

contingency_table works

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 12
    statistic    df p.value method                     effectsize        estimate
        <dbl> <int>   <dbl> <chr>                      <chr>                <dbl>
  1      8.74     2  0.0126 Pearson's Chi-squared test Cramer's V (adj.)    0.464
    conf.level conf.low conf.high conf.method conf.distribution n.obs
         <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
  1       0.99        0     0.937 ncp         chisq                32

Code
  df1[["expression"]]
Output
  [[1]]
  list(chi["Pearson"]^2 * "(" * 2 * ")" == "8.74073", italic(p) == 
      "0.01265", widehat(italic("V"))["Cramer"] == "0.46431", CI["99%"] ~ 
      "[" * "0.00000", "0.93683" * "]", italic("n")["obs"] == "32")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 12
    statistic    df   p.value method                     effectsize       
        <dbl> <int>     <dbl> <chr>                      <chr>            
  1      457.     1 2.30e-101 Pearson's Chi-squared test Cramer's V (adj.)
    estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
       <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
  1    0.455       0.95    0.413     0.497 ncp         chisq              2201

Code
  df2[["expression"]]
Output
  [[1]]
  list(chi["Pearson"]^2 * "(" * 1 * ")" == "456.87", italic(p) == 
      "2.30e-101", widehat(italic("V"))["Cramer"] == "0.46", CI["95%"] ~ 
      "[" * "0.41", "0.50" * "]", italic("n")["obs"] == "2,201")

Code
  select(df3, -expression)
Output
  # A tibble: 1 x 12
    statistic    df p.value method                     effectsize        estimate
        <dbl> <int>   <dbl> <chr>                      <chr>                <dbl>
  1      15.8    15   0.399 Pearson's Chi-squared test Cramer's V (adj.)   0.0558
    conf.level conf.low conf.high conf.method conf.distribution n.obs
         <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
  1       0.99        0     0.252 ncp         chisq                52

Code
  df3[["expression"]]
Output
  [[1]]
  list(chi["Pearson"]^2 * "(" * 15 * ")" == "15.75", italic(p) == 
      "0.40", widehat(italic("V"))["Cramer"] == "0.06", CI["99%"] ~ 
      "[" * "0.00", "0.25" * "]", italic("n")["obs"] == "52")

paired contingency_table works

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 11
    statistic    df  p.value method                     effectsize estimate
        <dbl> <dbl>    <dbl> <chr>                      <chr>         <dbl>
  1      13.3     1 0.000261 McNemar's Chi-squared test Cohen's g     0.333
    conf.level conf.low conf.high conf.method n.obs
         <dbl>    <dbl>     <dbl> <chr>       <int>
  1       0.95    0.164     0.427 binomial      100

Code
  df1[["expression"]]
Output
  [[1]]
  list(chi["McNemar"]^2 * "(" * 1 * ")" == "13.33333", italic(p) == 
      "0.00026", widehat(italic("g"))["Cohen"] == "0.33333", CI["95%"] ~ 
      "[" * "0.16436", "0.42663" * "]", italic("n")["pairs"] == 
      "100")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 11
    statistic    df  p.value method                     effectsize estimate
        <dbl> <dbl>    <dbl> <chr>                      <chr>         <dbl>
  1      13.3     1 0.000261 McNemar's Chi-squared test Cohen's g     0.333
    conf.level conf.low conf.high conf.method n.obs
         <dbl>    <dbl>     <dbl> <chr>       <int>
  1        0.9    0.229       0.5 binomial       95

Code
  df2[["expression"]]
Output
  [[1]]
  list(chi["McNemar"]^2 * "(" * 1 * ")" == "13.333", italic(p) == 
      "2.607e-04", widehat(italic("g"))["Cohen"] == "0.333", CI["90%"] ~ 
      "[" * "0.229", "0.500" * "]", italic("n")["pairs"] == "95")

Goodness of Fit contingency_table works without counts

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 12
    statistic    df p.value method                                   effectsize 
        <dbl> <dbl>   <dbl> <chr>                                    <chr>      
  1      1.12     1   0.289 Chi-squared test for given probabilities Pearson's C
    estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
       <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
  1    0.184       0.99        0     0.541 ncp         chisq                32

Code
  df1[["expression"]]
Output
  [[1]]
  list(chi["gof"]^2 * "(" * 1 * ")" == "1.12500", italic(p) == 
      "0.28884", widehat(italic("C"))["Pearson"] == "0.18429", 
      CI["99%"] ~ "[" * "0.00000", "0.54074" * "]", italic("n")["obs"] == 
          "32")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 12
    statistic    df   p.value method                                   effectsize 
        <dbl> <dbl>     <dbl> <chr>                                    <chr>      
  1      722.     1 3.92e-159 Chi-squared test for given probabilities Pearson's C
    estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
       <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
  1    0.497       0.95    0.474         1 ncp         chisq              2201

Code
  df2[["expression"]]
Output
  [[1]]
  list(chi["gof"]^2 * "(" * 1 * ")" == "722.45", italic(p) == "3.92e-159", 
      widehat(italic("C"))["Pearson"] == "0.50", CI["95%"] ~ "[" * 
          "0.47", "1.00" * "]", italic("n")["obs"] == "2,201")

Code
  select(df3, -expression)
Output
  # A tibble: 1 x 12
    statistic    df     p.value method                                  
        <dbl> <dbl>       <dbl> <chr>                                   
  1      33.8     3 0.000000223 Chi-squared test for given probabilities
    effectsize  estimate conf.level conf.low conf.high conf.method
    <chr>          <dbl>      <dbl>    <dbl>     <dbl> <chr>      
  1 Pearson's C    0.555       0.95    0.385     0.658 ncp        
    conf.distribution n.obs
    <chr>             <int>
  1 chisq                76

Code
  df3[["expression"]]
Output
  [[1]]
  list(chi["gof"]^2 * "(" * 3 * ")" == "33.76", italic(p) == "2.23e-07", 
      widehat(italic("C"))["Pearson"] == "0.55", CI["95%"] ~ "[" * 
          "0.38", "0.66" * "]", italic("n")["obs"] == "76")

bayesian (proportion test)

Code
  select(df1, -expression)
Output
  # A tibble: 1 x 3
     bf10 prior.scale method                                     
    <dbl>       <dbl> <chr>                                      
  1 0.247           1 Bayesian one-way contingency table analysis

Code
  df1[["expression"]]
Output
  [[1]]
  list(log[e] * (BF["01"]) == "1.40", italic("a")["Gunel-Dickey"] == 
      "1.00")

Code
  select(df2, -expression)
Output
  # A tibble: 1 x 3
     bf10 prior.scale method                                     
    <dbl>       <dbl> <chr>                                      
  1 0.579          10 Bayesian one-way contingency table analysis

Code
  df2[["expression"]]
Output
  [[1]]
  list(log[e] * (BF["01"]) == "0.55", italic("a")["Gunel-Dickey"] == 
      "10.00")

bayesian (contingency tab)

Code
  df1[["expression"]]
Output
  [[1]]
  list(log[e] * (BF["01"]) == "-2.82", widehat(italic("V"))["Cramer"]^"posterior" == 
      "0.41", CI["95%"]^ETI ~ "[" * "0.00", "0.68" * "]", italic("a")["Gunel-Dickey"] == 
      "1.00")

Code
  df2[["expression"]]
Output
  [[1]]
  list(log[e] * (BF["01"]) == "3.29", widehat(italic("V"))["Cramer"]^"posterior" == 
      "0.00", CI["95%"]^ETI ~ "[" * "0.00", "0.26" * "]", italic("a")["Gunel-Dickey"] == 
      "1.00")