Performs a 'standardized' Multiple Correspondence Analysis, i.e it takes MCA results and forces all the dimensions to be orthogonal to a supplementary 'control' variable.
an object of class
a list of 'control' variables
Standardized MCA unfolds in several steps. First, for each dimension of an input MCA, individual coordinates are used as dependent variable in a linear regression model and the 'control' variable is included as covariate in the same model. The residuals from every models are retained and bound together. The resulting data frame is composed of continuous variables and its number of columns is equal to the number of dimensions in the input MCA. Lastly, this data frame is used as input in a Principal Component Analysis.
Returns an object of class "stMCA". This object will be similar to
resmca argument, still it does not comprehend modified rates,
categories contributions and variables contributions.
Robette, Bry and Roueff, 2014, "Un dialogue de sourds dans le theatre statistique? Analyse geometrique des donnees et effets de structure", [http://nicolas.robette.free.fr/publis.html], forthcoming.
1 2 3 4 5 6 7 8 9 10
## Performs a specific MCA on 'Music' example data set ## ignoring every 'NA' (i.e. 'not available') categories, ## and then performs a 'standardized' MCA controlling for age. data(Music) mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15)) plot(mca) textvarsup(mca,Music$Age,col='red') stmca <- stMCA(mca,control=list(Music$Age)) plot(stmca) textvarsup(stmca,Music$Age,col='red')
Loading required package: FactoMineR Loading required package: nleqslv Loading required package: nnet
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.