PA_FA {EFA.dimensions} R Documentation

## 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=NULL, Ncases=NULL, 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', 'gamma', and 'polychoric'. Required only if the entered data is not a correlation matrix. Nfactors The number of factors to extract. Ncases The number of cases. Required only if data is a correlation matrix. iterpaf The maximum number of iterations. 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

### Value

A list with the following elements:

 totvarexplNOROT The eigenvalues and total variance explained totvarexplROT The rotation sums of squared loadings and total variance explained for the rotated loadings 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

### Author(s)

Brian P. O'Connor

### Examples


# the Harman (1967) correlation matrix
PA_FA(data_Harman, corkind='pearson', Nfactors = 2, Ncases=305, iterpaf = 50,
rotate='PROMAX', ppower = 4, verbose=TRUE)

# Rosenberg Self-Esteem scale items
PA_FA(data_RSE, corkind='polychoric', Nfactors = 2, iterpaf = 50,
rotate='PROMAX', ppower = 4, verbose=TRUE)

# NEO-PI-R scales
PA_FA(data_NEOPIR, corkind='pearson', Nfactors = 5, iterpaf = 50,
rotate='PROMAX', ppower = 4, verbose=TRUE)



[Package EFA.dimensions version 0.1.7.4 Index]