score.pca {ade4}R Documentation

Graphs to Analyse a factor in PCA

Description

performs the canonical graph of a Principal Component Analysis.

Usage

## S3 method for class 'pca':
score(x, xax = 1, which.var = NULL, mfrow = NULL, csub = 2, 
    sub = names(x$tab), abline = TRUE, ...)

Arguments

x an object of class pca
xax the column number for the used axis
which.var the numbers of the kept columns for the analysis, otherwise all columns
mfrow a vector of the form "c(nr,nc)", otherwise computed by a special own function n2mfrow
csub a character size for sub-titles, used with par("cex")*csub
sub a vector of string of characters to be inserted as sub-titles, otherwise the names of the variables
abline a logical value indicating whether a regression line should be added
... further arguments passed to or from other methods

Author(s)

Daniel Chessel

Examples

data(deug)
dd1 <- dudi.pca(deug$tab, scan = FALSE)
score(dd1, csub = 3)
 
# The correlations are :
dd1$co[,1]
# [1] 0.7925 0.6532 0.7410 0.5287 0.5539 0.7416 0.3336 0.2755 0.4172

Worked out examples


> library(ade4)
> ### Name: score.pca
> ### Title: Graphs to Analyse a factor in PCA
> ### Aliases: score.pca
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(deug)
> dd1 <- dudi.pca(deug$tab, scan = FALSE)
> score(dd1, csub = 3)
>  
> # The correlations are :
> dd1$co[,1]
[1] -0.7924753 -0.6531896 -0.7410261 -0.5287294 -0.5538660 -0.7416171 -0.3336153
[8] -0.2755026 -0.4171874
> # [1] 0.7925 0.6532 0.7410 0.5287 0.5539 0.7416 0.3336 0.2755 0.4172
> 
> 
> 
> 

[Package ade4 Index]