biplot.prm: Biplot for prm objects

In sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

Description

This biplot for prm objects visualizes the original variables which contribute to the model and their impact on the latent components as well as the position of the observations in the transformed space. The data is projected onto two of the latent components.

Usage

## S3 method for class 'prm'
biplot(x, comps = c(1, 2), 
colors = list(scores = "#0000AA", loadings = "red", background = "#BBBBEE"), 
textsize = 6, arrowshapes = c(25, 0.03), labelpos=0.35, ...)

Arguments

x	object of class prm.
comps	vector with two integers, referring to the components to be plotted.
colors	list of three elements named scores, loadings and background with color codes or names of colors.
textsize	the text size in which to print the scores and loading names.
arrowshapes	vector of length two containing the angle of the arrowheads and their relative length in npc.
labelpos	numeric value; determines distance of the arrow label to the arrowhead.
...	further arguments. Currently not used.

Author(s)

Sven Serneels, BASF Corp.

References

Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.

Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.

See Also

plot.prm, prms, biplot.sprm

Examples

set.seed(5023)
U <- c(rep(3,20), rep(4,30))
X <- replicate(6, U+rnorm(50))
beta <- c(rep(1, 3), rep(-1,3))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
d <- as.data.frame(X)
d$y <- y
mod <- prms(y~., data=d, a=2, fun="Hampel")
biplot(mod, comps = c(1, 2))

sprm documentation built on May 2, 2019, 9:57 a.m.