The pcaMethods package offers a collection of different algorithms for PCA, some of which (NIPALS and others) can be used on data that have missing values.
Kevin Wright On Nov 24, 2007 4:59 PM, Hartmut Oldenbürger <[EMAIL PROTECTED]> wrote: > Hi Birgit, and All > > Possibly you should not consider the case completed ;-) > There is an important alternative to imputing means, or estimates from > first or second order > regression (Frane, Psychometrica, BMDP): partial-least-squares (Wold), > which uses as much > information from the data as possible to estimate the principal > components, or the missing data. > > Stephane Dray, also from Lyon, provides 'nipals' here: > http://biomserv.univ-lyon1.fr/~dray/software.php > There is also an interesting paper. - In case, you use PLS to estimate > and impute, set the > number of factors as high as reasonably possible, e.g. m-1, when m is > the number of variables. > best - Hartmut Oldenbürger > --- > [EMAIL PROTECTED] > http://www.wipaed.wiso.uni-goettingen.de/~holdenb1 > > > ______________________________________________ > 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. > ______________________________________________ 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.
