Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Principal Component Analysis (pca)

Description

Principal Component Analysis

Usage

pca(
  data,
  vars,
  nFactorMethod = "parallel",
  nFactors = 1,
  minEigen = 1,
  rotation = "varimax",
  hideLoadings = 0.3,
  sortLoadings = FALSE,
  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
sortLoadings	TRUE or FALSE (default), sort the factor loadings by size
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

Output

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 = vars(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
#