Principal Component Analysis
Principal Component Analysis
Example usage
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
#
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 |
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)