FactoMineR (version 1.33)

HMFA: Hierarchical Multiple Factor Analysis

Description

Performs a hierarchical multiple factor analysis, using an object of class list of data.frame.

Usage

HMFA(X,H,type = rep("s", length(H[[1]])), ncp = 5, graph = TRUE, axes = c(1,2), name.group = NULL)

Arguments

X

a data.frame

H

a list with one vector for each hierarchical level; in each vector the number of variables or the number of group constituting the group

type

the type of variables in each group in the first partition; three possibilities: "c" or "s" for quantitative variables (the difference is that for "s", the variables are scaled in the program), "n" for categorical variables; by default, all the variables are quantitative and the variables are scaled unit

ncp

number of dimensions kept in the results (by default 5)

graph

boolean, if TRUE a graph is displayed

axes

a length 2 vector specifying the components to plot

name.group

a list of vector containing the name of the groups for each level of the hierarchy (by default, NULL and the group are named L1.G1, L1.G2 and so on)

Value

Returns a list including:

eig

a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance

group

a list with first a list of matrices with the coordinates of the groups for each level and second a matrix with the canonical correlation (correlation between the coordinates of the individuals and the partial points))

ind

a list of matrices with all the results for the active individuals (coordinates, square cosine, contributions)

quanti.var

a list of matrices with all the results for the quantitative variables (coordinates, correlation between variables and axes)

quali.var

a list of matrices with all the results for the supplementary categorical variables (coordinates of each categories of each variables, and v.test which is a criterion with a Normal distribution)

partial

a list of arrays with the coordinates of the partial points for each partition

References

Le Dien, S. & Pags, J. (2003) Hierarchical Multiple factor analysis: application to the comparison of sensory profiles, Food Quality and Preferences, 18 (6), 453-464.

See Also

print.HMFA, plot.HMFA, dimdesc

Examples

data(wine)
hierar <- list(c(2,5,3,10,9,2), c(4,2))
res.hmfa <- HMFA(wine, H = hierar, type=c("n",rep("s",5)))