indsup.Rd
From 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
#>