ggadd_partial {GDAtools} | R Documentation |
Main and partial effect of a supplementary variable
Description
Adds the main and partial effects of a supplementary variable to a MCA cloud of individuals, with one or more supplementary partialled out
Usage
ggadd_partial(p, resmca, var, controls, excl = NULL,
axes = c(1,2), col = "black", textsize = 4, lines = TRUE, dashes = TRUE,
legend = "right", force = 1, max.overlaps = Inf)
Arguments
p |
|
resmca |
object created with |
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. |
axes |
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). |
col |
the color for the labels and lines. Default is "black". |
textsize |
size of the labels of categories. Default is 4. |
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. |
legend |
the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right. |
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. |
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
Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).
Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).
See Also
ggcloud_variables
, ggadd_supvar
, ggadd_supvars
, ggadd_interaction
Examples
# specific MCA of Taste example data set
data(Taste)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA",
"Comedy.NA", "Crime.NA", "Animation.NA", "SciFi.NA", "Love.NA",
"Musical.NA")
mca <- speMCA(Taste[,1:11], excl = junk)
# effect of education,
# with age partialled out (partial effect) or not (main effect)
p <- ggcloud_indiv(mca, col = "grey95")
ggadd_partial(p, mca, Taste$Educ, Taste$Age)