R: Principal Component Analysis

pca {jmv} | R Documentation

## Principal Component Analysis

### Description

Principal Component Analysis

### Usage

    pca(data, vars, nFactorMethod = "parallel", nFactors = 1, minEigen = 1,
      rotation = "varimax", hideLoadings = 0.3, screePlot = FALSE,
      eigen = FALSE, factorCor = FALSE, factorSummary = FALSE, kmo = FALSE,
      bartlett = FALSE)

### Arguments

`data` | the data as a data frame
`vars` | a vector of strings naming the variables of interest in `data`
`nFactorMethod` | `'parallel'` (default), `'eigen'` or `'fixed'`, the way to determine the number of factors
`nFactors` | an integer (default: 1), the number of components in the model
`minEigen` | a number (default: 1), the minimal eigenvalue for a component to be included in the model
`rotation` | `'none'`, `'varimax'` (default), `'quartimax'`, `'promax'`, `'oblimin'`, or `'simplimax'`, the rotation to use in estimation
`hideLoadings` | a number (default: 0.3), hide loadings below this value
`screePlot` | `TRUE` or `FALSE` (default), show scree plot
`eigen` | `TRUE` or `FALSE` (default), show eigenvalue table
`factorCor` | `TRUE` or `FALSE` (default), show factor correlations
`factorSummary` | `TRUE` or `FALSE` (default), show factor summary
`kmo` | `TRUE` or `FALSE` (default), show Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy (MSA) results
`bartlett` | `TRUE` or `FALSE` (default), show Bartlett's test of sphericity results

### Value

A results object containing:

`results$loadings` |  |  |  |  | a table
`results$factorStats$factorSummary` |  |  |  |  | a table
`results$factorStats$factorCor` |  |  |  |  | a table
`results$modelFit$fit` |  |  |  |  | a table
`results$assump$bartlett` |  |  |  |  | a table
`results$assump$kmo` |  |  |  |  | a table
`results$eigen$initEigen` |  |  |  |  | a table
`results$eigen$screePlot` |  |  |  |  | an image

Tables can be converted to data frames with `asDF` or `as.data.frame`. For example:

`results$loadings$asDF`

`as.data.frame(results$loadings)`

### Examples

    data('iris')

    pca(iris, vars = c('Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width'))

    #
    #  PRINCIPAL COMPONENT ANALYSIS
    #
    #  Component Loadings
    #  ----------------------------------------
    #                    1         Uniqueness
    #  ----------------------------------------
    #    Sepal.Length     0.890        0.2076
    #    Sepal.Width     -0.460        0.7883
    #    Petal.Length     0.992        0.0168
    #    Petal.Width      0.965        0.0688
    #  ----------------------------------------
    #    Note. 'varimax' rotation was used
    #

[Package jmv version 0.8.6.2 Index]