dimdescr.RdIdentifies the variables and the categories that are the most characteristic according to each dimension obtained by a MCA. It is inspired by dimdesc function in FactoMineR package (see Husson et al, 2010), but allows to analyze variants of MCA, such as specific MCA or class specific MCA.
dimdescr(resmca, vars = NULL, dim = c(1,2),
limit = NULL, correlation = "pearson",
na.rm.cat = FALSE, na.value.cat = "NA", na.rm.cont = FALSE,
nperm = NULL, distrib = "asympt",
shortlabs = TRUE)object of class MCA, speMCA, csMCA, stMCA or multiMCA
data frame of variables to describes the MCA dimensions with. If NULL (default), the active variables of the MCA will be used.
the dimensions which are described. Default is c(1,2)
for the relationship between a dimension and a categorical variable, only associations (measured with point-biserial correlations) higher or equal to limit will be displayed. If NULL (default), they are all displayed.
character string. The type of correlation measure to be used between two numerical variables : "pearson" (default), "spearman" or "kendall".
logical, indicating whether NA values in the categorical variables should be silently removed before the computation proceeds. If FALSE (default), an additional level is added to the categorical variables (see na.value.cat argument).
character string. Name of the level for NA category. Default is "NA". Only used if na.rm.cat = FALSE.
logical indicating whether NA values in the numerical variables should be silently removed before the computation proceeds. Default is FALSE.
numeric. Number of permutations for the permutation tests of independence. If NULL (default), no permutation test is performed.
the null distribution of permutation test of independence can be approximated by its asymptotic distribution ("asympt", default) or via Monte Carlo resampling ("approx").
logical. If TRUE (default), the data frame will have short column names, so that all columns can be displayed side by side on a laptop screen.
See condesc.
Returns a list of ncp lists including:
associations between dimensions of the MCA and the variables in vars
a data frame with categorical variables from vars and associations measured by correlation coefficients
Husson, F., Le, S. and Pages, J. (2010). Exploratory Multivariate Analysis by Example Using R, Chapman and Hall.
# specific MCA on 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)
# description of the dimensions
dimdescr(mca, limit = 0.1, nperm = 10)
#> $dim.1
#> $dim.1$variables
#> variable measure association pvalue
#> 1 Jazz Eta2 0.611 0.00000
#> 2 Classical Eta2 0.539 0.00000
#> 3 Rock Eta2 0.160 0.00000
#> 4 Rap Eta2 0.075 0.00000
#> 5 FrenchPop Eta2 0.012 0.00158
#>
#> $dim.1$categories
#> categories avg.coord.in.cat sd.coord.in.cat sd.coord.in.dim cor
#> 1 Jazz.No 0.204 0.324 0.528 0.751
#> 2 Classical.No 0.244 0.335 0.528 0.710
#> 3 Rock.No 0.131 0.471 0.528 0.398
#> 4 Rap.Yes 0.339 0.504 0.528 0.274
#> 5 FrenchPop.No 0.072 0.543 0.528 0.108
#> 6 FrenchPop.Yes -0.044 0.511 0.528 -0.103
#> 7 Rap.No -0.062 0.510 0.528 -0.264
#> 8 Rock.Yes -0.344 0.514 0.528 -0.396
#> 9 Classical.Yes -0.615 0.414 0.528 -0.734
#> 10 Jazz.Yes -0.849 0.340 0.528 -0.780
#> pval
#> 1 0.00000
#> 2 0.00000
#> 3 0.00000
#> 4 0.00000
#> 5 0.00168
#> 6 0.00004
#> 7 0.00000
#> 8 0.00000
#> 9 0.00000
#> 10 0.00000
#>
#>
#> $dim.2
#> $dim.2$variables
#> variable measure association pvalue
#> 1 Rap Eta2 0.526 0.0000
#> 2 Rock Eta2 0.456 0.0000
#> 3 Classical Eta2 0.077 0.0000
#> 4 Jazz Eta2 0.031 0.0000
#> 5 FrenchPop Eta2 0.003 0.4893
#>
#> $dim.2$categories
#> categories avg.coord.in.cat sd.coord.in.cat sd.coord.in.dim cor pval
#> 1 Rap.No 0.147 0.320 0.467 0.704 0e+00
#> 2 Rock.No 0.192 0.339 0.467 0.660 0e+00
#> 3 Classical.Yes 0.205 0.445 0.467 0.276 0e+00
#> 4 Jazz.No 0.040 0.443 0.467 0.165 3e-05
#> 5 Jazz.Yes -0.169 0.526 0.467 -0.175 0e+00
#> 6 Classical.No -0.083 0.451 0.467 -0.273 0e+00
#> 7 Rock.Yes -0.519 0.363 0.467 -0.676 0e+00
#> 8 Rap.Yes -0.793 0.318 0.467 -0.724 0e+00
#>
#>