lmdme (version 1.14.0)

biplot: Plot a biplot of a lmdme object

Description

Plot a biplot over each decomposed "pca" or "plsr" present in lmdme component object's slot.

Usage

"biplot"(x, comp=1:2, xlab=NULL, ylab=NULL, term=NULL, mfcol, xlabs, ylabs, which, ...)

Arguments

x

lmdme class object.

comp

a two component vector with the PC components to plot. Default comp=1:2.

xlab

character for the x-label title for PCA biplots.

ylab

character for the y-label title for PCA biplots.

term

character with the corresponding term/s for biploting. Default value is NULL in order to obtain every available biplot/s.

mfcol

numeric vector for par layout. If missing mfcol=c(1,2) will be used if more than one biplot is available. Use mfcol==NULL to override par call inside biplot function.

xlabs,ylabs

vector of character strings to label the first/second set of points. The default is to use dimname of "x"/"y", or "1:n" if the dimname is NULL for the respective set of points. If a single character is passed e.g. "o", the same character is used for all the points.

which

character to indicate the type of biplot to use when plsr decomposition is applied. Default value is "x" (X scores and loadings), "y" for (Y scores and loadings), "scores" (X and Y scores) or "loadings" (X and Y loadings). See biplot.mvr for details.

...

additional parameters for biplot.prcomp(pca) or biplot.mvr(plsr)

Value

plotted biplot/s of the component/s of the given lmdme object. If par() is called before this function, the biplots can be arranged in the same window

See Also

prcomp, plsr, biplot.princomp, biplot.mvr

Examples

{
data(stemHypoxia)

##Just to make a balanced dataset in the Fisher sense (2 samples per
## time*oxygen levels)
design<-design[design$time %in% c(0.5,1,5) & design$oxygen %in% c(1,5,21), ]
design$time<-as.factor(design$time)
design$oxygen<-as.factor(design$oxygen)
rownames(M)<-M[, 1]

#Keeping appropriate samples only
M<-M[, colnames(M) %in% design$samplename]

##ANOVA decomposition
fit<-lmdme(model=~time+oxygen+time:oxygen, data=M, design=design)

##ASCA for all the available terms, over those subjects/genes where at least
##one interaction coefficient is statistically different from zero (F-test
##on coefficients).
id<-F.p.values(fit, term="time:oxygen")<0.001
decomposition(fit, decomposition="pca",scale="row",subset=id)

## Not run: 
# ##Does not call par inside
# par(mfrow=c(2,2))
# biplot(fit, xlabs="o", mfcol=NULL)
# 
# ##Just the term of interest
# biplot(fit, xlabs="o", term="time")
# 
# ##In separate graphics
# biplot(fit, xlabs="o", term=c("time", "oxygen"), mfcol=c(1,1))
# 
# ##All terms in the same graphic
# biplot(fit, xlabs="o", mfcol=c(1,3))
# ## End(Not run)
}

##Now using plsr on interaction coefficients
decomposition(fit, decomposition="plsr", term="time:oxygen", scale="row",
subset=id)

## Not run: 
# par(mfrow=c(2,2))
# 
# ##plsr biplot by default which="x"
# biplot(fit, which="x", mfcol=NULL)
# 
# ##Other alternatives to which
# biplot(fit, which="y", mfcol=NULL)
# biplot(fit, which="scores", mfcol=NULL)
# biplot(fit, which="loadings", mfcol=NULL, xlabs="o")
# ## End(Not run)