jmv (version 0.7.3.1)

pca: 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

Examples

data('iris')

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

#
#  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
#
#