EFA.dimensions (version 0.1.6)

PA_FA: Principal axis (common) factor analysis

Description

Principal axis (common) factor analysis with squared multiple correlations as the initial communality estimates

Usage

PA_FA(data, corkind, Nfactors, iterpaf, rotate, ppower, verbose)

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', and 'polychoric'. Required only if the entered data is not a correlation matrix.

Nfactors

The number of factors to extract.

iterpaf

The maximum number of iterations.

rotate

The factor rotation method. The options are: 'promax', 'varimax', 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? The options are: TRUE (default) or FALSE.

Value

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)

Examples

# NOT RUN {
PA_FA(data_RSE, corkind="pearson", Nfactors = 2, iterpaf = 50, 
      rotate='promax', ppower=3, verbose=TRUE)
# }