reduceDimensionality {psichomics} | R Documentation

## Reduce dimensionality after processing missing values from data frame

### Description

Reduce dimensionality after processing missing values from data frame

### Usage

    reduceDimensionality(data, type = c("pca", "ica"), center = TRUE,
      scale. = FALSE, naTolerance = NULL, missingValues = round(0.05 *
      ncol(data)), ...)

### Arguments

`data` | Data frame: data
`type` | Character: dimensionality reduction technique (`pca` or `ica`)
`center` | either a logical value or numeric-alike vector of length equal to the number of columns of `x`, where ‘numeric-alike’ means that `as.numeric(.)` will be applied successfully if `is.numeric(.)` is not true.
`scale.` | Boolean: scale variables?
`naTolerance` | Integer: percentage of tolerated missing values per column (deprecated)
`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)
`...` | Extra parameters passed to FUN

### Value

PCA result in a `prcomp` object or ICA result object

[Package psichomics version 1.6.2 Index]