MCA {FactoMineR} | R Documentation |
Performs Multiple Correspondence Analysis (MCA) with supplementary individuals, supplementary quantitative
variables and supplementary qualitative variables.
Missing values are treated as an additional level,
MCA(X, ncp = 5, ind.sup = NULL, quanti.sup = NULL, quali.sup = NULL, graph = TRUE, axes = c(1,2), row.w = NULL)
X |
a data frame with n rows (individuals) and p columns (categorical variables) |
ncp |
number of dimensions kept in the results (by default 5) |
ind.sup |
a vector indicating the indexes of the supplementary individuals |
quanti.sup |
a vector indicating the indexes of the quantitative supplementary variables |
quali.sup |
a vector indicating the indexes of the qualitative supplementary variables |
graph |
boolean, if TRUE a graph is displayed |
axes |
a length 2 vector specifying the components to plot |
row.w |
an optional row weights (by default, uniform row weights) |
Returns a list including:
eig |
a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance |
var |
a list of matrices containing all the results for the active variables (coordinates, square cosine, contributions, v.test) |
ind |
a list of matrices containing all the results for the active individuals (coordinates, square cosine, contributions) |
ind.sup |
a list of matrices containing all the results for the supplementary individuals (coordinates, square cosine) |
quanti.sup |
a matrix containing the coordinates of the supplementary quantitative variables (the correlation between a variable and an axis is equal to the variable coordinate on the axis) |
quali.sup |
a list of matrices with all the results for the supplementary qualitative variables (coordinates of each categories of each variables, square cosine and v.test which is a criterion with a Normal distribution) |
call |
a list with some statistics |
Returns the individuals factor map and the variables factor map.
Jeremy Mazet, Francois Husson Francois.Husson@agrocampus-ouest.fr
## Not run: data (poison) res.mca = MCA (poison, quali.sup = 3:4, quanti.sup = 1:2) ## End(Not run)