Test of group differences

Source: R/groupdiff.R

One-way analysis (ANOVA or Kruskal-Wallis Test) with post-hoc comparisons and plots

groupdiff(
  formula,
  data,
  method = c("anova", "kw"),
  digits = 2,
  horizontal = FALSE,
  posthoc = TRUE
)

Arguments

formula

use the form Y ~ X where Y is the response variable, and X is the categorical explanatory variable. X will be coerced to be a factor.
data

a data frame.
method

character. Either "anova", or "kw" (see details).
digits

Number of significant digits to print.
horizontal

logical. If TRUE, boxplots are plotted horizontally.
posthoc

logical. If TRUE, the default, perform pairwise post-hoc comparisons (TukeyHSD for ANOVA and Conover Test for Kuskal Wallis). This test will only be performed if there are 3 or more levels for X.

Value

a list with 3 components:

result

omnibus test

summarystats

summary statistics

plot

ggplot2 graph

Details

The groupdiff function performs one of two analyses:

anova

A one-way analysis of variance, with TukeyHSD post-hoc comparisons.

kw

A Kruskal Wallis Rank Sum Test, with Conover Test post-hoc comparisons.

In each case, summary statistics and a grouped boxplots are provided. In the parametric case, the statistics are n, mean, and standard deviation. In the nonparametric case the statistics are n, median, and median absolute deviation. If posthoc = TRUE, pairwise comparisons of superimposed on the boxplots. Groups that share a letter are not significantly different (p < .05), controlling for multiple comparisons.

See also

kwAllPairsConoverTest, multcompLetters.

Examples

# parametric analysis
groupdiff(hp ~ gear, mtcars)

#> $result
#> [1] "F(2, 29) = 11.42, p-value = 0.00022"
#> 
#> $summarystats
#>   gear  n   mean     sd
#> 1    4 12  89.50  25.89
#> 2    3 15 176.13  47.69
#> 3    5  5 195.60 102.83
#> 
#> $plot
#> 

# nonparametric analysis
groupdiff(popularity ~ vehicle_style, cardata,
          method="kw", horizontal=TRUE)

#> Warning: Ties are present. Quantiles were corrected for ties.
#> $result
#> [1] "Chi-squared =  666.48 , df = 15 , p-value = <2e-16"
#> 
#> $summarystats
#>          vehicle_style    n median     mad
#> 1      Convertible SUV   29    481    0.00
#> 2        2dr Hatchback  506    873  647.90
#> 3              4dr SUV 2488    870  907.35
#> 4                Sedan 3048    873  991.86
#> 5          Convertible  793    873  759.09
#> 6        4dr Hatchback  702   1385  957.76
#> 7                Coupe 1211   1439 1218.70
#> 8                Wagon  592    873  736.85
#> 9    Passenger Minivan  417   1385  957.76
#> 10     Crew Cab Pickup  681   1851  690.89
#> 11 Extended Cab Pickup  623   1851  690.89
#> 12  Regular Cab Pickup  392   1385  925.14
#> 13             2dr SUV  138   1385 1239.45
#> 14           Cargo Van   95   1851 1930.35
#> 15       Cargo Minivan   71   2009 2164.60
#> 16       Passenger Van  128   5657    0.00
#> 
#> $plot
#>