homog.test.RdFrom MCA results, computes a homogeneity test between categories of a supplementary variable, i.e. characterizes the homogeneity of several subclouds.
homog.test(resmca, var, dim = c(1,2))object of class MCA, speMCA, csMCA, stMCA or multiMCA
the categorical supplementary variable. It does not need to have been used at the MCA step.
the axes which are described. Default is c(1,2)
Returns a list of lists, one for each selected dimension in the MCA. Each list has 2 elements :
The square matrix of test statistics
The square matrix of p-values
Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).
Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).
# specific MCA of Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# homogeneity test for variable Age
homog.test(mca, Music$Age)
#> $dim.1
#> $dim.1$test.stat
#> 15-24 25-49 50+
#> 15-24 0.000000 3.7253364 4.2265912
#> 25-49 3.725336 0.0000000 0.6334962
#> 50+ 4.226591 0.6334962 0.0000000
#>
#> $dim.1$p.values
#> 15-24 25-49 50+
#> 15-24 1.000000e+00 0.0001950549 2.372582e-05
#> 25-49 1.950549e-04 1.0000000000 5.264096e-01
#> 50+ 2.372582e-05 0.5264096384 1.000000e+00
#>
#>
#> $dim.2
#> $dim.2$test.stat
#> 15-24 25-49 50+
#> 15-24 0.000000 4.096013 9.711800
#> 25-49 4.096013 0.000000 7.556267
#> 50+ 9.711800 7.556267 0.000000
#>
#> $dim.2$p.values
#> 15-24 25-49 50+
#> 15-24 1.000000e+00 4.203268e-05 0.000000e+00
#> 25-49 4.203268e-05 1.000000e+00 4.152234e-14
#> 50+ 0.000000e+00 4.152234e-14 1.000000e+00
#>
#>