PCA {FactoMineR} | R Documentation |
Performs Principal Component Analysis (PCA) with supplementary individuals, supplementary quantitative
variables and supplementary qualitative variables.
Missing values are replaced by the column mean.
PCA(X, scale.unit = TRUE, ncp = 5, ind.sup = NULL, quanti.sup = NULL, quali.sup = NULL, row.w = NULL, col.w = NULL, graph = TRUE, axes = c(1,2))
X |
a data frame with n rows (individuals) and p columns (numeric variables) |
ncp |
number of dimensions kept in the results (by default 5) |
scale.unit |
a boolean, if TRUE (value set by default) then data are scaled to unit variance |
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 |
row.w |
an optional row weights (by default, uniform row weights) |
col.w |
an optional column weights (by default, uniform column weights) |
graph |
boolean, if TRUE a graph is displayed |
axes |
a length 2 vector specifying the components to plot |
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, correlation between variables and axes, square cosine, contributions) |
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 list of matrices containing all the results for the supplementary quantitative variables (coordinates, correlation between variables and axes) |
quali.sup |
a list of matrices containing all the results for the supplementary qualitative variables (coordinates of each categories of each variables, and v.test which is a criterion with a Normal distribution) |
Returns the individuals factor map and the variables factor map.
Jeremy Mazet, Francois Husson Francois.Husson@agrocampus-ouest.fr
data(decathlon) res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup=13) ## plot of the eigenvalues ## barplot(res.pca$eig[,1],main="Eigenvalues",names.arg=1:nrow(res.pca$eig)) plot(res.pca,choix="ind",habillage=13) dimdesc(res.pca, axes = 1:2) ## To draw ellipses around the categories of the 13th variable (which is categorical) aa=cbind.data.frame(decathlon[,13],res.pca$ind$coord) bb=coord.ellipse(aa,bary=TRUE) plot.PCA(res.pca,habillage=13,ellipse=bb)