Performs Dual Multiple Factor Analysis (DMFA) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables.
DMFA(don, num.fact = ncol(don), scale.unit = TRUE, ncp = 5,
quanti.sup = NULL, quali.sup = NULL, graph = TRUE, axes=c(1,2))a data frame with n rows (individuals) and p columns (numeric variables)
the number of the categorical variable which allows to make the group of individuals
a boolean, if TRUE (value set by default) then data are scaled to unit variance
number of dimensions kept in the results (by default 5)
a vector indicating the indexes of the quantitative supplementary variables
a vector indicating the indexes of the categorical supplementary variables
boolean, if TRUE a graph is displayed
a length 2 vector specifying the components to plot
Returns a list including:
a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
a list of matrices containing all the results for the active variables (coordinates, correlation between variables and axes, square cosine, contributions)
a list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions)
a list of matrices containing all the results for the supplementary individuals (coordinates, square cosine)
a list of matrices containing all the results for the supplementary quantitative variables (coordinates, correlation between variables and axes)
a list of matrices containing 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)
the result of the singular value decomposition
a list with the partial coordinate of the variables for each group
a list with the data centered by group
a list with the results for the groups (cordinate, normalized coordinates, cos2)
a list with the covariance matrices for each group
Returns the individuals factor map and the variables factor map.
# NOT RUN {
## Example with the famous Fisher's iris data
res.dmfa = DMFA ( iris, num.fact = 5)
# }
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