Principal components analysis
Principal components analysis
PCA(data, corkind, Nfactors=NULL, Ncases=NULL, rotate, ppower, verbose)
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 extract. |
Ncases |
The number of cases. Required only if data is a correlation matrix. |
rotate |
The factor rotation method. The options are: 'PROMAX', , and 'none'. |
ppower |
The power value to be used in a promax rotation (required only if rotate = 'PROMAX'). Suggested value: 3 |
verbose |
Should detailed results be displayed in console? TRUE (default) or FALSE |
A list with the following elements:
eigenvalues |
The eigenvalues |
loadingsNOROT |
The unrotated factor loadings |
loadingsROT |
The rotated factor loadings (for varimax rotation) |
structure |
The structure matrix (for promax rotation) |
pattern |
The pattern matrix (for promax rotation) |
correls |
The correlations between the factors (for promax rotation) |
cormat_reproduced |
The reproduced correlation matrix, based on the rotated loadings |
fit_coefficients |
Model fit coefficients |
Brian P. O'Connor
# the Harman (1967) correlation matrix PCA(data_Harman, Nfactors=2, Ncases=305, rotate='PROMAX', ppower = 4, verbose=TRUE) # Rosenberg Self-Esteem scale items PCA(data_RSE, corkind='polychoric', Nfactors=2, rotate='PROMAX', ppower = 4, verbose=TRUE) # NEO-PI-R scales PCA(data_NEOPIR, corkind='pearson', Nfactors=5, rotate='PROMAX', ppower = 4, verbose=TRUE)
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