Description Usage Arguments Value Note Author(s) See Also Examples
Grouped data visualisation by PCA, MDS, PCADA and PLSDA.
1 2 3 4 5 6 7 8 9 10 | pca.plot.wrap(data.list,title="plotting",...)
mds.plot.wrap(data.list,method="euclidean",title="plotting",...)
pca.plot.wrap(data.list,title="plotting",...)
lda.plot.wrap.1(data.list,title="plotting",...)
pls.plot.wrap(data.list,title="plotting",...)
|
data.list |
A two-layer list structure, in which the second
layer include a data frame called |
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
|
title |
A part of title string for plotting. |
... |
Further arguments to |
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.
There is a slight differences between lda.plot.wrap.1
and
lda.plot.wrap
. The former plots the two-class grouped data,
which has one linear discriminant (LD1), with strip plot. The later
plots the two-class data by LD1 vs LD2 which is identical to LD1.
Hence lda.plot.wrap
is more general and can be applied to
fusion of two and more class data sets.
Wanchang Lin
pcaplot
, mdsplot
, plot.pcalda
,
plot.plsc
, dat.sel
, grpplot
,
panel.elli.1
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | 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]]
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.