SensoMineR (version 1.03)

pca: Principal components analysis

Description

Performs a PCA and returns the individuals factor map and the variables factor map.

Usage

pca(df, supind = NULL, supvar = NULL,
   row.w = rep(1,nrow(df)-length(supind))/(nrow(df)-length(supind)),
   scale.unit = TRUE, coord = c(1,2), graph = TRUE, 
   main.title = NULL, clabel = 1, cex = 0.7, font = 1, csub = 1, 
   col = "black", lty = 1)

Arguments

df

a data frame with n rows (individuals) and p columns (numeric variables)

supind

indexes of the illustrative individuals

supvar

indexes of the illustrative variables

row.w

an optional row weights (by default, uniform row weights)

scale.unit

a boolean, if TRUE (value set by default) then data are scaled to unit variance

coord

a length 2 vector specifying the components to plot

graph

boolean, if TRUE a graph is displayed

main.title

the title of the graph

clabel

if not NULL, a character size for the labels, used with par("cex")*clabel

cex

cf. function par in the graphics package

font

cf. function par in the graphics package

csub

a character size for the legend, used with par("cex")*csub

col

color of the variables

lty

line type of the arrows

Value

• Returns a list including:
• eig
  a numeric vector with the all eigenvalues
• li
  a matrix with the coordinates of rows
• co
  a matrix with the coordinates of columns
• lisup
  a matrix with the coordinates of illustrative rows
• cosup
  a matrix with the coordinates of illustrative columns
• Returns the individuals factor map and the variables factor map.

Examples

data(chocolates)
resaverage<-averagetable(chocolates, formul = "~Product+Panelist", 
    firstvar = 5)
pca(resaverage, scale.unit = TRUE)