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Run Pairwise Wilcoxon Tests and Return a Reporting Table

Source: R/GLM_MEANS.R
report_wtests.Rd

Performs Wilcoxon rank-sum tests for each selected dependent variable against each selected independent variable, across all pairwise level combinations of the independent variable. Also computes descriptive statistics, effect sizes, Bartlett homogeneity results, and Bonferroni adjustment.

In simple terms: this function builds a full nonparametric comparison table, similar to report_ttests, but using wilcox.test for group differences.

Usage

report_wtests(df, dv, iv, file = NULL, ...)

Arguments

df

A data frame containing both the independent and dependent variables.

dv

Integer vector of column indices for the continuous dependent variables.

iv

Integer vector of column indices for the categorical independent variables.

file

output filename

...

Further arguments passed to stats::wilcox.test() for every comparison, e.g. alternative, mu, exact or correct. The formula and data are built by the function and conf.int = TRUE is always used, so do not pass x, y, formula, data, paired or conf.int.

Value

A data frame where each row is one pairwise level comparison for one dependent-independent variable combination. Returned columns mean:

  • DV: Name stored in the DV column by the current implementation. Note: this currently contains the independent variable name.

  • IV: Name stored in the IV column by the current implementation. Note: this currently contains the dependent variable name.

  • level1: First group level being compared.

  • level2: Second group level being compared.

  • n1: Sample size in level1.

  • n2: Sample size in level2.

  • W: Wilcoxon test statistic from wilcox.test.

  • p: p-value from wilcox.test.

  • CI_l: Lower confidence interval bound from wilcox.test.

  • CI_u: Upper confidence interval bound from wilcox.test.

  • alternative: Alternative hypothesis used by wilcox.test.

  • method: Test label from wilcox.test.

  • mean1: Mean of the dependent variable in level1.

  • mean2: Mean of the dependent variable in level2.

  • sd1: Standard deviation in level1.

  • sd2: Standard deviation in level2.

  • sd_pooled: Pooled standard deviation, sqrt((sd1^2 + sd2^2) / 2).

  • d: Cohen d effect size, abs(mean2 - mean1) / sd_pooled.

  • r: Wilcoxon effect size from compute_wilcoxon_effect_size.

  • k_squared[bartlett]: Bartlett test statistic for equal variances.

  • df[bartlett]: Degrees of freedom of Bartlett test.

  • p[bartlett]: p-value of Bartlett test. Small values suggest heteroscedasticity.

  • bonferroni_p: Bonferroni-adjusted alpha threshold computed for the number of tests in the output table.

  • significant: Logical-like character flag (TRUE / FALSE) indicating whether p is below bonferroni_p.

Details

Missing values are removed per analysis pair using complete cases on the current dependent and independent variables.

For each independent variable, all pairwise level combinations are tested using utils::combn.

The function calls stats::wilcox.test with conf.int = TRUE and passes any additional arguments on through ....

The function also calls report_dataframe to generate a formatted report.

Examples

report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2
)
#>          DV  IV level1 level2 n1 n2    W        p CI_l CI_u alternative                                            method mean1 mean2   sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before sex Female   Male 60 60 1262 0.004721  -10   -2   two.sided Wilcoxon rank sum test with continuity correction 153.6 159.3 10.74 11.41     11.08 0.5084 0.2579              0.2192
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397         0.05        TRUE
report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2:3
)
#>          DV     IV level1 level2 n1 n2      W           p CI_l CI_u alternative                                            method mean1 mean2    sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before    sex Female   Male 60 60 1261.5 0.004720545  -10   -2   two.sided Wilcoxon rank sum test with continuity correction 153.6 159.3 10.736 11.41     11.08 0.5084 0.2579              0.2192
#> 2 bp_before agegrp  30-45  46-59 40 40  667.0 0.202247153   -7    2   two.sided                      Wilcoxon rank sum exact test 151.7 155.1  9.258 11.46     10.42 0.3288 0.1426              1.7393
#> 3 bp_before agegrp  30-45    60+ 40 40  355.0 0.000009609  -16   -7   two.sided                      Wilcoxon rank sum exact test 151.7 162.6  9.258 10.73     10.02 1.0879 0.4948              0.8322
#> 4 bp_before agegrp  46-59    60+ 40 40  484.5 0.002113691  -13   -3   two.sided                      Wilcoxon rank sum exact test 155.1 162.6 11.460 10.73     11.10 0.6735 0.3437              0.1679
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397       0.0125        TRUE
#> 2            1      0.1872       0.0125       FALSE
#> 3            1      0.3616       0.0125        TRUE
#> 4            1      0.6820       0.0125        TRUE
report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2:3,
  alternative = "two.sided"
)
#>          DV     IV level1 level2 n1 n2      W           p CI_l CI_u alternative                                            method mean1 mean2    sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before    sex Female   Male 60 60 1261.5 0.004720545  -10   -2   two.sided Wilcoxon rank sum test with continuity correction 153.6 159.3 10.736 11.41     11.08 0.5084 0.2579              0.2192
#> 2 bp_before agegrp  30-45  46-59 40 40  667.0 0.202247153   -7    2   two.sided                      Wilcoxon rank sum exact test 151.7 155.1  9.258 11.46     10.42 0.3288 0.1426              1.7393
#> 3 bp_before agegrp  30-45    60+ 40 40  355.0 0.000009609  -16   -7   two.sided                      Wilcoxon rank sum exact test 151.7 162.6  9.258 10.73     10.02 1.0879 0.4948              0.8322
#> 4 bp_before agegrp  46-59    60+ 40 40  484.5 0.002113691  -13   -3   two.sided                      Wilcoxon rank sum exact test 155.1 162.6 11.460 10.73     11.10 0.6735 0.3437              0.1679
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397       0.0125        TRUE
#> 2            1      0.1872       0.0125       FALSE
#> 3            1      0.3616       0.0125        TRUE
#> 4            1      0.6820       0.0125        TRUE
report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2:3,
  alternative = "less"
)
#>          DV     IV level1 level2 n1 n2      W           p CI_l CI_u alternative                                            method mean1 mean2    sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before    sex Female   Male 60 60 1261.5 0.002360273 -Inf   -2        less Wilcoxon rank sum test with continuity correction 153.6 159.3 10.736 11.41     11.08 0.5084 0.2579              0.2192
#> 2 bp_before agegrp  30-45  46-59 40 40  667.0 0.101123577 -Inf    1        less                      Wilcoxon rank sum exact test 151.7 155.1  9.258 11.46     10.42 0.3288 0.1426              1.7393
#> 3 bp_before agegrp  30-45    60+ 40 40  355.0 0.000004805 -Inf   -7        less                      Wilcoxon rank sum exact test 151.7 162.6  9.258 10.73     10.02 1.0879 0.4948              0.8322
#> 4 bp_before agegrp  46-59    60+ 40 40  484.5 0.001056845 -Inf   -4        less                      Wilcoxon rank sum exact test 155.1 162.6 11.460 10.73     11.10 0.6735 0.3437              0.1679
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397       0.0125        TRUE
#> 2            1      0.1872       0.0125       FALSE
#> 3            1      0.3616       0.0125        TRUE
#> 4            1      0.6820       0.0125        TRUE
report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2:3,
  alternative = "greater"
)
#>          DV     IV level1 level2 n1 n2      W      p CI_l CI_u alternative                                            method mean1 mean2    sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before    sex Female   Male 60 60 1261.5 0.9977   -9  Inf     greater Wilcoxon rank sum test with continuity correction 153.6 159.3 10.736 11.41     11.08 0.5084 0.2579              0.2192
#> 2 bp_before agegrp  30-45  46-59 40 40  667.0 0.8997   -7  Inf     greater                      Wilcoxon rank sum exact test 151.7 155.1  9.258 11.46     10.42 0.3288 0.1426              1.7393
#> 3 bp_before agegrp  30-45    60+ 40 40  355.0 1.0000  -15  Inf     greater                      Wilcoxon rank sum exact test 151.7 162.6  9.258 10.73     10.02 1.0879 0.4948              0.8322
#> 4 bp_before agegrp  46-59    60+ 40 40  484.5 0.9990  -13  Inf     greater                      Wilcoxon rank sum exact test 155.1 162.6 11.460 10.73     11.10 0.6735 0.3437              0.1679
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397       0.0125       FALSE
#> 2            1      0.1872       0.0125       FALSE
#> 3            1      0.3616       0.0125       FALSE
#> 4            1      0.6820       0.0125       FALSE
report_wtests(
  df = df_blood_pressure,
  dv = which("bp_before" == names(df_blood_pressure)),
  iv = 2:3,
  var.equal = TRUE,
  file = "wilcoxontest"
)
#>          DV     IV level1 level2 n1 n2      W           p CI_l CI_u alternative                                            method mean1 mean2    sd1   sd2 sd_pooled      d      r k_squared[bartlett]
#> 1 bp_before    sex Female   Male 60 60 1261.5 0.004720545  -10   -2   two.sided Wilcoxon rank sum test with continuity correction 153.6 159.3 10.736 11.41     11.08 0.5084 0.2579              0.2192
#> 2 bp_before agegrp  30-45  46-59 40 40  667.0 0.202247153   -7    2   two.sided                      Wilcoxon rank sum exact test 151.7 155.1  9.258 11.46     10.42 0.3288 0.1426              1.7393
#> 3 bp_before agegrp  30-45    60+ 40 40  355.0 0.000009609  -16   -7   two.sided                      Wilcoxon rank sum exact test 151.7 162.6  9.258 10.73     10.02 1.0879 0.4948              0.8322
#> 4 bp_before agegrp  46-59    60+ 40 40  484.5 0.002113691  -13   -3   two.sided                      Wilcoxon rank sum exact test 155.1 162.6 11.460 10.73     11.10 0.6735 0.3437              0.1679
#>   df[bartlett] p[bartlett] bonferroni_p significant
#> 1            1      0.6397       0.0125        TRUE
#> 2            1      0.1872       0.0125       FALSE
#> 3            1      0.3616       0.0125        TRUE
#> 4            1      0.6820       0.0125        TRUE