performPCA {psichomics} | R Documentation

## Perform principal component analysis after processing missing values

### Description

Perform principal component analysis after processing missing values

### Usage

    performPCA(data, center = TRUE, scale. = FALSE,
      missingValues = round(0.05 * nrow(data)), ...)

### Arguments

`data` | an optional data frame (or similar: see `model.frame`) containing the variables in the formula `formula`. By default the variables are taken from `environment(formula)`.
`center` | a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of `x` can be supplied. The value is passed to `scale`.
`scale.` | a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is `FALSE` for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of `x` can be supplied. The value is passed to `scale`.
`missingValues` | Integer: number of tolerated missing values per column to be replaced with the mean of the values of that same column (5 rows by default)
`...` | Arguments passed on to `stats::prcomp`

### Value

PCA result in a `prcomp` object

### See Also

`plotPCA`, `performICA` and `plotICA`

### Examples

    performPCA(USArrests)

[Package psichomics version 1.10.2 Index]