PLSbiplot1 (version 0.1)

PLS.GLM.biplot_no_labels: The Partial Least Squares (PLS) biplot for Generalized Linear Model (GLM), with the labels of the samples, coefficient points and tick markers excluded

Description

Takes in a set of predictor variables and a set of response variables and produces a PLS biplot for the (univariate) GLMs, with the labels of the samples, coefficient points and tick markers excluded.

Usage

PLS.GLM.biplot_no_labels(X, y, algorithm = NULL, ax.tickvec.X = NULL, ax.tickvec.y = NULL, ax.tickvec.b = NULL, ...)

Arguments

X

A (NxP) predictor matrix

y

A (Nx1) response vector

algorithm

The PLS.GLM algorithm

ax.tickvec.X

tick marker length for each X-variable axis in the biplot

ax.tickvec.y

tick marker length for the y-variable axis in the biplot

ax.tickvec.b

(purple) tick marker length for the y-variable axis in the biplot

...

Other arguments. Currently ignored

Value

The PLS biplot of a GLM of D=[X y] with some parameters

Examples

if(require(robustbase))
possum.mat
y = as.matrix(possum.mat[,1], ncol=1)
dimnames(y) = list(paste("S", 1:nrow(possum.mat), seq=""), "Diversity")
X = as.matrix(possum.mat[,2:14], ncol=13)
dimnames(X) = list(paste("S", 1:nrow(possum.mat), seq=""), colnames(possum.mat[,2:14]))
PLS.GLM.biplot_no_labels(X, y, algorithm=PLS.GLM, ax.tickvec.X=rep(5,ncol(X)),
ax.tickvec.y=10, ax.tickvec.b=7)

#Pima.tr data
if(require(MASS))
data(Pima.tr, package="MASS")
X = as.matrix(cbind(Pima.tr[,1:7]))
dimnames(X) = list(1:nrow(X), colnames(X))
y = as.matrix(as.numeric(Pima.tr$type)-1, ncol=1)
#0=No and 1=Yes
dimnames(y) = list(1:nrow(y), paste("type"))
PLS.GLM.biplot_no_labels(X, y, algorithm=PLS.binomial.GLM,
ax.tickvec.X=c(3,3,8,7,8,5,2), ax.tickvec.y=3, ax.tickvec.b=3)