DAQ | R Documentation |
Descriptive discriminant analysis (aka "Analyse Factorielle Discriminante" for the French school of multivariate data analysis) with qualitative variables.
DAQ(data, class, excl = NULL, row.w = NULL,
type = "FR", select = TRUE)
data |
data frame with only categorical variables |
class |
factor specifying the class |
excl |
numeric vector indicating the indexes of the "junk" categories (default is NULL). See |
row.w |
numeric vector of row weights. If NULL (default), a vector of 1 for uniform row weights is used. |
type |
character string. If "FR" (default), the inverse of the total covariance matrix is used as metric. If "GB", it is the inverse of the within-class covariance matrix (Mahalanobis metric), which makes the results equivalent to linear discriminant analysis as implemented in |
select |
logical. If TRUE (default), only a selection of components of the MCA are used for the discriminant analysis step. The selected components are those corresponding to eigenvalues higher of equal to 1/Q, with Q the number of variables in |
This approach is also known as "disqual" and was developed by G. Saporta (see references). It consists in two steps : 1. Multiple Correspondence Analysis of the data 2. Discriminant analysis of the components from the MCA
The results are the same with type
"FR" or "GB", only the eigenvalues vary. With type="FR"
, these eigenvalues vary between 0 and 1 and can be interpreted as "discriminant power".
An object of class PCA
from FactoMineR
package, with class
as qualitative supplementary variable and the disjunctive table of data
as quantitative supplementary variables, and two additional items :
cor_ratio |
correlation ratios between |
mca |
an object of class |
If there are NAs in data
, these NAs will be automatically considered as junk categories. If one desires more flexibility, data
should be recoded to add explicit factor levels for NAs and then excl
option may be used to select the junk categories.
Nicolas Robette
Bry X., 1996, Analyses factorielles multiples, Economica.
Lebart L., Morineau A. et Warwick K., 1984, Multivariate Descriptive Statistical Analysis, John Wiley and sons, New-York.)
Saporta G., 1977, "Une méthode et un programme d'analyse discriminante sur variables qualitatives", Premières Journées Internationales, Analyses des données et informatiques, INRIA, Rocquencourt.
Saporta G., 2006, Probabilités, analyses des données et statistique, Editions Technip.
DA
, bcMCA
, MCAiv
, speMCA
library(FactoMineR)
data(tea)
res <- DAQ(tea[,1:18], tea$SPC)
# plot of observations colored by class
plot(res, choix = "ind", invisible = "quali",
label = "quali", habillage = res$call$quali.sup$numero)
# plot of class categories
plot(res, choix = "ind", invisible = "ind", col.quali = "black")
# plot of the variables in data
plot(res, choix = "var", invisible = "var")
# plot of the components of the MCA
plot(res, choix = "varcor", invisible = "quanti.sup")
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