Plot of Main and Partial Effect

Plots the the main and partial effects of a supplementary variable for a PLS regression, with one or more supplementary partialled out.

plo_part(object, var, controls, excl = NULL,
  comps = c(1,2), shapesize = 1.5, col = "black",
  textsize = 4, force = 1, max.overlaps = Inf,
  lines = TRUE, dashes = TRUE, alpha = 0.3, legend = "right")

Arguments

object

an object of class mvr from pls package

var

factor. The categorical supplementary variable.

controls

data frame of supplementary variables to be partialled out (i.e. control variables)

excl

character vector of categories from the var to exclude from the plot. If NULL (default), all the supplementary categories are plotted.

comps

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

shapesize

Size of the shapes. Default is 1.5.

col

the color for the labels and lines. Default is "black".

textsize

Size of the labels of categories. Default is 4.

force

Force of repulsion between overlapping text labels. Defaults to 1. If 0, labels are not repelled at all.

max.overlaps

Exclude text labels that overlap too many things. Defaults to Inf, which means no labels are excluded.

lines

logical. Whether to add colored lines between the points of the categories of v1. Default is TRUE.

dashes

logical. Whether to add gray dashed lines between the points of the categories of v2. Default is TRUE.

alpha

Numerical value. Transparency of the partial effects. Default is 0.3.

legend

the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right.

Value

a ggplot2 object

Note

The partial effects of the supplementary variable are computed with the Average Marginal Effects of a linear regression, with individual coordinates as dependent variable, and the supplementary and control variables as independent variables.

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

See also

plo_sup, plo_inter

Examples

library(pls)
data(mpg, package = "ggplot2")
pls <- mvr(displ ~ cty + hwy + cyl,
           ncomp = 3,
           data = mpg,
           validation = "CV",
           method = "oscorespls")
plo_part(pls, factor(mpg$class), factor(mpg$trans), lines = FALSE, dashes = FALSE)
#> Warning: Using alpha for a discrete variable is not advised.