R: Screeplot of PCA Results

screeplot {stats} | R Documentation

## Screeplot of PCA Results

### Description

`screeplot` plots the variances against the number of the principal component. This is also the `plot` method for class `"princomp"`.

### Usage

    screeplot(x, npcs = min(10, length(x$sdev)),
              type = c("barplot", "lines"),
              main = deparse(substitute(x)), ...)

### Arguments

`x` | an object of class `"princomp"`, as from `princomp()`.
`npcs` | the number of principal components to be plotted.
`type` | the type of plot.
`main, ...` | graphics parameters.

### References

Mardia, K. V., J. T. Kent and J. M. Bibby (1979). Multivariate Analysis, London: Academic Press.

Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S, Springer-Verlag.

### See Also

`princomp`.

### Examples

    ## The variances of the variables in the
    ## USArrests data vary by orders of magnitude, so scaling is appropriate
    (pc.cr <- princomp(USArrests, cor = TRUE))  # inappropriate
    screeplot(pc.cr)

    fit <- princomp(covmat=Harman74.cor)
    screeplot(fit)
    screeplot(fit, npcs=24, type="lines")

[Package stats version 2.4.1 Index]