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
`...` | Arguments passed on to `stats::prcomp`

### Value

PCA result in a `prcomp` object

### See Also

Other functions to analyse principal components: `calculateLoadingsContribution()`, `plotPCAvariance()`, `plotPCA()`

### Examples

    performPCA(USArrests)

[Package psichomics version 1.20.2 Index]