| tabcontrib {GDAtools} | R Documentation |
Table with the main contributions of categories to an axis
Description
Identifies the categories that contribute the most to a given dimension of a Multiple Correspondence Analysis and organizes these informations into a fancy table.
Usage
tabcontrib(resmca, dim = 1, best = TRUE, dec = 2, shortlabs = FALSE)Arguments
resmca |
object of class |
dim |
dimension to describe (default is 1st dimension) |
best |
if FALSE, displays all the categories; if TRUE (default), displays only categories with contributions higher than average |
dec |
integer. The number of decimals for the results (default is 2) |
shortlabs |
logical. If TRUE, the data frame will have short column names, so that all columns can be displayed side by side on a laptop screen. Default is FALSE (long explicit column names). |
Value
A data frame with the following contributions.:
Variable |
names of the variables |
Category |
names of the categories |
Weight |
weights of the categories |
Quality of representation |
quality of representation (squared cosine) of the categories on the axis |
Contribution (left) |
contributions of the categories located on one side of the axis |
Contribution (right) |
contributions of the categories located on the other side of the axis |
Total contribution |
contributions summed by variable |
Cumulated contribution |
cumulated sum of the contributions |
Contribution of deviation |
for each variable, contribution of the deviation between the barycenter of the categories located on one side of the axis and the barycenter of those located on the other side |
Proportion to variable |
contribution of deviation expressed as a proportion of the contribution of the variable |
Author(s)
Nicolas Robette
References
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).
See Also
dimcontrib, dimdescr, dimeta2, dimtypicality
Examples
# 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)
# main contributions on axis 1
tabcontrib(mca, 1)
# main contributions on axis 2
tabcontrib(mca, 2)