data.visualisation: Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA

Description

Grouped data visualisation by PCA, MDS, PCADA and PLSDA.

Usage

pca_plot_wrap(data.list,title="plotting",...) 
  mds_plot_wrap(data.list,method="euclidean",title="plotting",...) 
  lda_plot_wrap(data.list,title="plotting",...) 
  lda_plot_wrap.1(data.list,title="plotting",...) 
  pls_plot_wrap(data.list,title="plotting",...)

Value

mds_plot_wrap returns a handle for MDS plot.

All other four functions return a list with components: the first one is an object of class "trellis" for data visualisation; the second one is also an object of class "trellis" but plotting the corresponding variables, PCs (principal components), LDs (linear discrimniants) and LCs (latent components); and the third one is a matrix of these variables.

Arguments

data.list: A two-layer list structure, in which the second layer include a data frame called dat and a factor of class label called cls. Noticed that names of the first layer of data.list should be given. data.list can be produced by dat.sel.
method: The distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given. It is only for mds_plot_wrap.
title: A part of title string for plotting.
...: Further arguments to lattice. See corresponding entry in xyplot for non-trivial details of lattice. One argument is ep: an integer flag for ellipse. 1 and 2 for plotting overall and group ellipse, respectively. Otherwise, none. For details, see panel.elli.1.

Author

Wanchang Lin

Examples

Run this code

data(iris)
x <- subset(iris, select = -Species)
y <- iris$Species
## generate data list by dat.sel
iris.pw <- dat.sel(x,y,choices=NULL)
names(iris.pw)

pca.p <- pca_plot_wrap(iris.pw, ep=2)
pca.p[[1]]     ## visualised by PCA
pca.p[[2]]     ## plot PCA variables
pca.p[[3]]     ## matrix of PCA variables

mds.p <- mds_plot_wrap(iris.pw)
mds.p

pls.p  <- pls_plot_wrap(iris.pw)
pls.p[[1]]
pls.p[[2]]
pls.p[[3]]

lda.p <- lda_plot_wrap.1(iris.pw)
lda.p[[1]]
lda.p[[2]]
lda.p[[3]]
lda_plot_wrap(iris.pw)$lda.p

## only plot iris data
ph <- pca_plot_wrap(list(list(dat=x, cls=y)))$pca.p  
## Not given data names
ph
update(ph, strip=FALSE)       ## strip is an argument of lattice

tmp <- list(iris.dat=list(dat=x, cls=y))
pca_plot_wrap(tmp)$pca.p
pca_plot_wrap(tmp,strip=FALSE)$pca.p
pls_plot_wrap(tmp,strip=FALSE)$pls.p
lda_plot_wrap(tmp,strip=FALSE)$lda.p

data(abr1)
cls <- factor(abr1$fact$class)
dat <- preproc(abr1$pos, method="log")
## pair-wise data set
dat.pw <- dat.sel(dat, cls,choices=c("2","3","4"))

## add mult-class
idx <- grep("2|3|4",cls)
cls.234 <- factor(cls[idx])
dat.234 <- dat[idx,,drop = FALSE]

## combine all
dat.tmp <- c(dat.pw, 
             "2~3~4"=list(list(dat=dat.234,cls=cls.234)),
             all=list(list(dat=dat, cls=cls)))

## PCA
ph <- pca_plot_wrap(dat.tmp, title="abr1", par.strip.text = list(cex=0.75), 
                     scales=list(cex =.75,relation="free"), ep=2) 
## See function grpplot for usage of ep.
ph[[1]]
ph[[2]]                     

##PLSDA
ph <- pls_plot_wrap(dat.tmp, title="abr1", par.strip.text = list(cex=0.75), 
                     scales=list(cex =.75,relation="free"), ep=2) 
ph[[1]]
ph[[2]]                     

## PCADA
ph <- lda_plot_wrap(dat.tmp, title="abr1", par.strip.text = list(cex=0.75), 
                     scales=list(cex =.75,relation="free")) 
ph[[1]]
ph[[2]]