performICA {psichomics} | R Documentation

## Perform independent component analysis after processing missing values

### Description

Perform independent component analysis after processing missing values

### Usage

    performICA(
      data,
      n.comp = min(5, ncol(data)),
      center = TRUE,
      scale. = FALSE,
      missingValues = round(0.05 * nrow(data)),
      alg.typ = c("parallel", "defaltion"),
      fun = c("logcosh", "exp"),
      alpha = 1,
      ...
    )

### 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)`.
`n.comp` | number of components to be extracted
`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
`alg.typ` | if `alg.typ == "parallel"` the components are extracted simultaneously (the default). if `alg.typ == "deflation"` the components are extracted one at a time.
`fun` | the functional form of the G function used in the approximation to neg-entropy (see ‘details’).
`alpha` | constant in range [1, 2] used in approximation to neg-entropy when `fun == "logcosh"`
`...` | Arguments passed on to `fastICA::fastICA`

### Value

ICA result in a `prcomp` object

### See Also

Other functions to analyse independent components: `plotICA()`

### Examples

    performICA(USArrests)

[Package psichomics version 1.16.0 Index]