indsup.RdFrom MCA results, computes statistics (coordinates, squared cosines) for supplementary individuals.
indsup(resmca, supdata)object of class 'MCA', 'speMCA' or 'csMCA'
data frame with the supplementary individuals. It must have the same factors as the data frame used as input for the initial MCA.
Returns a list:
matrix of individuals' coordinates
matrix of individuals' square cosines
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).
## Performs a specific MCA on 'Music' example data set
## ignoring every 'NA' (i.e. 'not available') categories,
## and then computes statistics for supplementary individuals.
data(Music)
getindexcat(Music)
#> [1] "FrenchPop.No" "FrenchPop.Yes" "FrenchPop.NA" "Rap.No"
#> [5] "Rap.Yes" "Rap.NA" "Rock.No" "Rock.Yes"
#> [9] "Rock.NA" "Jazz.No" "Jazz.Yes" "Jazz.NA"
#> [13] "Classical.No" "Classical.Yes" "Classical.NA" "Gender.Men"
#> [17] "Gender.Women" "Age.15-24" "Age.25-49" "Age.50+"
#> [21] "OnlyMus.Daily" "OnlyMus.Often" "OnlyMus.Rare" "OnlyMus.Never"
#> [25] "Daily.No" "Daily.Yes"
mca <- speMCA(Music[3:nrow(Music),1:5],excl=c(3,6,9,12,15))
indsup(mca,Music[1:2,1:5])
#> $coord
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> 2124 -0.03021772 -0.3899678 0.2212868 0.6879359 -0.1936124
#> 4485 -0.55418126 -0.6285349 -0.7238657 -0.5821206 0.1128835
#>
#> $cos2
#> dim.1 dim.2 dim.3 dim.4 dim.5
#> 2124 0.001281 0.213379 0.068708 0.664035 0.052597
#> 4485 0.194654 0.250390 0.332104 0.214775 0.008076
#>