Principal Component Analysis

Principal Component Analysis

Example usage


pca(iris, vars = vars(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


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


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

Tables can be converted to data frames with asDF or For example: