jmv

Principal Component Analysis

Principal Component Analysis

Example usage

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
#
#

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

Returns

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

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

results$loadings$asDF

as.data.frame(results$loadings)