Table with the main contributions of categories to an axis

Identifies the categories that contribute the most to a given dimension of a Multiple Correspondence Analysis and organizes these informations into a fancy table.

tabcontrib(resmca, dim = 1,
best = TRUE, limit = NULL,
dec = 2, shortlabs = FALSE)

Arguments

resmca: object created with MCA, speMCA, csMCA, wcMCA or bcMCA function
dim: dimension to describe (default is 1st dimension)
best: if FALSE, displays all the categories; if TRUE (default), displays only categories which contribute the most (see limit argument below)
limit: numerical value between 0 and 100. If best = TRUE (see above), only categories with percentage of contribution higher or equal to limit are displayed. If best = TRUE and limit = NULL (default), only categories with contributions higher or equal to average are displayed.
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

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).

Author

Nicolas Robette

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)
#>    Variable Category Weight Quality of representation
#> 2 Classical      Yes    142                     0.539
#> 1                 No    351                     0.505
#> 3      Jazz      Yes     95                     0.608
#>   Contribution (negative side) Contribution (positive side) Total contribution
#> 2                        27.72                                           38.52
#> 1                                                      10.8                   
#> 3                        35.34                                           35.34
#>   Cumulated contribution Contribution of deviation Proportion to variable
#> 2                  38.52                     38.52                  99.99
#> 1                                                                        
#> 3                  73.87                     35.34                  80.59
# main contributions on axis 2
tabcontrib(mca, 2)
#>   Variable Category Weight Quality of representation
#> 3     Rock      Yes    135                     0.456
#> 2                No    360                     0.435
#> 1      Rap      Yes     77                     0.525
#>   Contribution (negative side) Contribution (positive side) Total contribution
#> 3                        30.57                                           41.76
#> 2                                                     11.19                   
#> 1                        40.74                                           40.74
#>   Cumulated contribution Contribution of deviation Proportion to variable
#> 3                  41.76                     41.75                    100
#> 2                                                                        
#> 1                  82.49                     40.74                  84.35