Plot of Interactions

Plots the interaction between two categorical supplementary variables for a PLS regression.

plo_inter(object, var1, var2, excl1 = NULL, excl2 = NULL,
  comps = c(1,2), shapesize = 1, textsize = 3,
  force = 1, max.overlaps = Inf,
  lines = TRUE, dashes = TRUE)

Arguments

object

an object of class mvr from pls package

var1

factor. The first categorical supplementary variable.

var2

factor. The second categorical supplementary variable.

excl1

character vector of categories from the var1 to exclude from the plot. If NULL (default), all the supplementary categories are plotted.

excl2

character vector of categories from the var2 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.

textsize

Size of the labels of categories. Default is 3.

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.

Value

a ggplot2 object

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_part

Examples

library(pls)
data(mpg, package = "ggplot2")
pls <- mvr(displ ~ cty + hwy + cyl,
           ncomp = 3,
           data = mpg,
           validation = "CV",
           method = "oscorespls")
supvars <- data.frame(class = factor(mpg$class), trans = factor(mpg$trans))
plo_inter(pls, factor(mpg$class), factor(mpg$trans), lines = FALSE, dashes = FALSE)
#> Warning: The shape palette can deal with a maximum of 6 discrete values because more
#> than 6 becomes difficult to discriminate
#> ℹ you have requested 7 values. Consider specifying shapes manually if you need
#>   that many of them.
#> Warning: Removed 6 rows containing missing values or values outside the scale range
#> (`geom_point()`).