| PCA {EFA.dimensions} | R Documentation |
Principal components analysis
Description
Principal components analysis
Usage
PCA(data, corkind='pearson', Nfactors=NULL, Ncases=NULL, rotation='promax',
ppower=3, verbose=TRUE, rotate)Arguments
data |
An all-numeric dataframe where the rows are cases & the columns are the variables, or a correlation matrix with ones on the diagonal.The function internally determines whether the data are a correlation matrix. |
corkind |
The kind of correlation matrix to be used if data is not a correlation matrix. The options are 'pearson', 'kendall', 'spearman', 'gamma', and 'polychoric'. Required only if the entered data is not a correlation matrix. |
Nfactors |
The number of components to extraction. If not specified, then the EMPKC procedure will be used to determine the number of components. |
Ncases |
The number of cases. Required only if data is a correlation matrix. |
rotation |
The factor rotation method for the analysis. The orthogonal rotation options are: 'varimax' (the default), 'quartimax', 'bentlerT', 'equamax', 'geominT', 'bifactorT', 'entropy', and 'none'. The oblique rotation options are: 'promax' (the default), 'quartimin', 'oblimin', 'oblimax', 'simplimax', 'bentlerQ', 'geominQ', 'bifactorQ', and 'none'. |
ppower |
The power value to be used in a promax rotation (required only if rotation = 'promax'). Suggested value: 3 |
verbose |
Should detailed results be displayed in console? TRUE (default) or FALSE |
rotate |
(Deprecated.) Use 'rotation' instead. |
Value
A list with the following elements:
loadingsNOROT |
The unrotated factor loadings |
loadingsROT |
The rotated factor loadings |
pattern |
The pattern matrix |
structure |
The structure matrix |
phi |
The correlations between the factors |
varexplNOROT1 |
The initial eigenvalues and total variance explained |
varexplROT |
The rotation sums of squared loadings and total variance explained for the rotated loadings |
cormat_reprod |
The reproduced correlation matrix, based on the rotated loadings |
fit_coeffs |
Model fit coefficients |
communalities |
The unrotated factor solution communalities |
uniquenesses |
The unrotated factor solution uniquenesses |
Author(s)
Brian P. O'Connor
Examples
# the Harman (1967) correlation matrix
PCA(data_Harman, Nfactors=2, Ncases=305, rotation='oblimin', verbose=TRUE)
# Rosenberg Self-Esteem scale items
PCA(data_RSE, corkind='polychoric', Nfactors=2, rotation='bifactorQ', verbose=TRUE)
# NEO-PI-R scales
PCA(data_NEOPIR, corkind='pearson', Nfactors=5, rotation='promax', ppower = 4, verbose=TRUE)