plo_cor {morepls}

Plot of correlations

Description

Plots the correlations between (X and Y) variables and the components (X scores) of a PLS regression.

Usage

plo_cor(object, comps = 1:2, which = "both", min.cor = NULL,
        lim = NULL, circles = NULL, col = NULL, size = 3.88)

Arguments

object

an object of class mvr from pls package

comps

the components to use. Default is c(1,2).

which

character string. If "both" (default), X and Y variables are plotted. If "X", only X variables are plotted. If "Y", only Y variables are plotted.

min.cor

numerical value. The minimal correlation with one or the other component for a variable to be plotted. If NULL (default), all the variables are plotted.

lim

numerical value. The limit of the scale (in absolute value). If NULL (default), the limits are automatically determined from the range of tha data.

circles

vector of numeric values. Circles are added to the plot at radiuses specified in circles. If NULL (default), no circle is plotted.

col

colors for the names of the variables. Only one value should be provided if which is "X" or "Y", a vector of two if which is "both". If NULL (default), colors are set automatically.

size

numerical value. The size of the names of the variables.

Value

a ggplot2 object

Note

This is what Tenenhaus calls the univariate interpretation of the PLS components, as opposed to the multivariate interpretation (see plo_var).

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.

See Also

get_cor, plo_var

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_cor(pls)
# plot with circles corresponding to
# correlations of 0.5 and 1
plo_cor(pls, lim = 1, circles = c(0.5, 1), col = c("pink", "purple"))