Plot of correlations

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

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).

References

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

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

Author

Nicolas Robette

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_cor(pls)
#> Warning: ggrepel: 128 unlabeled data points (too many overlaps). Consider increasing max.overlaps

# 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"))
#> Warning: ggrepel: 131 unlabeled data points (too many overlaps). Consider increasing max.overlaps