HMFA: Hierarchical Multiple Factor Analysis

In FactoMineR: Multivariate Exploratory Data Analysis and Data Mining

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

Author(s)

Sebastien Le, Francois Husson [e-mail]

References

Le Dien, S. & Pages, 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)))

FactoMineR documentation built on May 29, 2024, 3:36 a.m.