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 -- View this message in context: http://r.789695.n4.nabble.com/How-to-use-PC1-of-PCA-and-dim1-of-MCA-as-a-predictor-in-logistic-regression-model-for-data-reduction-tp3750251p3753437.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
