plot_factors {EMMIXmfa} | R Documentation |
Plot functions for factor scores.
plot_factors(scores, type = "Umean", clust=if (exists('clust', where = scores)) scores$clust else NULL, limx = NULL, limy = NULL)
scores |
A list containing factor scores specified by
|
type |
What type of factor scores are to be plotted. See Details. |
clust |
Indicators of belonging to components. If available, they will be
portrayed in plots.
If not provided, looks for |
limx |
Numeric vector. Values in |
limy |
Numeric vector. Values in |
When the factor scores were obtained using mcfa
or mctfa
, then a visualization of the group structure
can be obtained by plotting the factor scores.
In the case of mfa
and mtfa
, the factor scores
simply corresponds to white noise.
The type
should either be "Uscores"
, "Uclust"
or
the default "Umean"
. See factor_scores
for a detailed
description of the factor scores.
Geoffrey McLachlan, Suren Rathnayake, Jungsun Baek
McLachlan GJ, Baek J, and Rathnayake SI (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. In Mixture Estimation and Applications, KL Mengersen, CP Robert, and DM Titterington (Eds). Hoboken, New Jersey: Wiley, pp. 171–191.
McLachlan GJ, and Peel D (2000). Finite Mixture Models. New York: Wiley.
# Visualizing data used in model estimation set.seed(1) inds <- dim(iris)[1] indSample <- sample(1 : inds, 50) model <- mcfa (iris[indSample, -5], g = 3, q = 2, nkmeans = 1, nrandom = 0, itmax = 150) minmis(model$clust, iris[indSample, 5]) #same as plot_factors(model, tyep = "Umean", clust = model$clust) plot(model) #can provide alternative groupings of samples via plot_factors plot_factors(model, clust = iris[indSample, 5]) #same as plot_factors(model, tyep = "Uclust") plot(model, type = "Uclust") Y <- iris[-c(indSample), -5] Y <- as.matrix(Y) clust <- predict(model, Y) minmis(clust, iris[-c(indSample), 5]) fac_scores <- factor_scores(model, Y) plot_factors(fac_scores, type = "Umean", clust = clust) plot_factors(fac_scores, type = "Umean", clust = iris[-c(indSample), 5])