[R] How to use PC1 of PCA and dim1 of MCA as a predictor in logistic regression model for data reduction

[Mark Difford]

On Aug 18, 2011 khosoda wrote:

> I'm trying to do model reduction for logistic regression.

Hi Kohkichi,

My general advice to you would be to do this by fitting a penalized logistic
model (see lrm in package rms and glmnet in package glmnet; there are
several others).

Other points are that the amount of variance explained by mixed PCA and MCA
are not comparable. Furthermore, homals() is a much better choice than MCA
because it handles different types of variables whereas MCA is for
categorical variables.

On the more specific question of whether you should use dudi.mix$l1 or
dudi.mix$li, it doesn't matter: the former is a scaled version of the
latter. Same for dudi.acm. To see this do the following:

##
plot(x18.dudi.mix$li[, 1], x18.dudi.mix$l1[, 1])

Regards, Mark.

-----
Mark Difford (Ph.D.)
Research Associate
Botany Department
Nelson Mandela Metropolitan University
Port Elizabeth, South Africa
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