## 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

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

```
> ### 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
>
>
>
>
```