Sampling-weighted principal component analysis
Computes principal components using the sampling weights.
svyprcomp(formula, design, center = TRUE, scale. = FALSE, tol = NULL, scores = FALSE, ...)
## S3 method for class 'svyprcomp'
biplot(x, cols=c("black","darkred"),xlabs=NULL,
weight=c("transparent","scaled","none"),
max.alpha=0.5,max.cex=0.5,xlim=NULL,ylim=NULL,pc.biplot=FALSE,
expand=1,xlab=NULL,ylab=NULL, arrow.len=0.1, ...)formula |
model formula describing variables to be used |
design |
survey design object. |
center |
Center data before analysis? |
scale. |
Scale to unit variance before analysis? |
tol |
Tolerance for omitting components from the results; a proportion of the standard deviation of the first component. The default is to keep all components. |
scores |
Return scores on each component? These are needed for |
x |
A |
cols |
Base colors for observations and variables respectively |
xlabs |
Formula, or character vector, giving labels for each observation |
weight |
How to display the sampling weights: |
max.alpha |
Opacity for the largest sampling weight, or for all points if |
max.cex |
Character size (as a multiple of |
xlim,ylim,xlab,ylab |
Graphical parameters |
expand,arrow.len |
See |
pc.biplot |
See |
... |
Other arguments to |
svyprcomp returns an object of class svyprcomp, similar to
class prcomp but including design information
data(api) dclus2<-svydesign(id=~dnum+snum, fpc=~fpc1+fpc2, data=apiclus2) pc <- svyprcomp(~api99+api00+ell+hsg+meals+emer, design=dclus2,scale=TRUE,scores=TRUE) pc biplot(pc, xlabs=~dnum, weight="none") biplot(pc, xlabs=~dnum,max.alpha=1) biplot(pc, weight="scaled",max.cex=1.5, xlabs=~dnum)
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