plo_part: Plot of Main and Partial Effect

In morepls: Interpretation Tools for Partial Least Squares Regression

Plot of Main and Partial Effect

Description

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

Usage

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.

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

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)

morepls documentation built on June 8, 2025, 10:34 a.m.