fa.sort: Sort factor analysis or principal components analysis...

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Although the print.psych function will sort factor analysis loadings, sometimes it is useful to do this outside of the print function. fa.sort takes the output from the fa or principal functions and sorts the loadings for each factor. Items are located in terms of their greatest loading. The new order is returned as an element in the fa list. fa.organize allows for the columns or rows to be reorganized.

Usage

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fa.sort(fa.results,polar=FALSE)
fa.organize(fa.results,o=NULL,i=NULL,cn=NULL,echelon=TRUE,flip=TRUE) 

Arguments

fa.results

The output from a factor analysis or principal components analysis using fa or principal. Can also just be a matrix of loadings.

polar

Sort by polar coordinates of first two factors (FALSE)

o

The order in which to order the factors

i

The order in which to order the items

cn

new factor names

echelon

Organize the factors so that they are in echelon form (variable 1 .. n on factor 1, n+1 ...n=k on factor 2, etc.)

flip

Flip factor loadings such that the colMean is positive.

Details

The fa.results$loadings are replaced with sorted loadings.

fa.organize takes a factor analysis or components output and reorganizes the factors in the o order. Items are organized in the i order. This is useful when comparing alternative factor solutions.

The flip option works only for the case of matrix input, not for full fa objects. Use the reflect function.

Value

A sorted factor analysis, principal components analysis, or omega loadings matrix.

These sorted values are used internally by the various diagram functions.

The values returned are the same as fa, except in sorted order. In addition, the order is returned as an additional element in the fa list.

Author(s)

William Revelle

See Also

See Also as fa,print.psych, fa.diagram,

Examples

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test.simple <- fa(sim.item(16),2)
fa.sort(test.simple)
fa.organize(test.simple,c(2,1))  #the factors but not the items have been rearranged

Example output

Loading required namespace: GPArotation
Factor Analysis using method =  minres
Call: fa(r = sim.item(16), nfactors = 2)
Standardized loadings (pattern matrix) based upon correlation matrix
      MR1   MR2   h2   u2 com
V5  -0.66  0.05 0.44 0.56   1
V6  -0.63 -0.06 0.41 0.59   1
V13  0.63 -0.06 0.40 0.60   1
V7  -0.61  0.01 0.37 0.63   1
V16  0.60  0.07 0.38 0.62   1
V15  0.60  0.03 0.36 0.64   1
V8  -0.59  0.03 0.34 0.66   1
V14  0.58  0.00 0.34 0.66   1
V4   0.04  0.63 0.40 0.60   1
V2   0.03  0.62 0.39 0.61   1
V1  -0.01  0.59 0.35 0.65   1
V9   0.00 -0.59 0.35 0.65   1
V3  -0.01  0.58 0.34 0.66   1
V12  0.06 -0.58 0.34 0.66   1
V11  0.01 -0.58 0.34 0.66   1
V10  0.00 -0.58 0.33 0.67   1

                       MR1  MR2
SS loadings           3.02 2.84
Proportion Var        0.19 0.18
Cumulative Var        0.19 0.37
Proportion Explained  0.51 0.49
Cumulative Proportion 0.51 1.00

 With factor correlations of 
     MR1  MR2
MR1 1.00 0.06
MR2 0.06 1.00

Mean item complexity =  1
Test of the hypothesis that 2 factors are sufficient.

The degrees of freedom for the null model are  120  and the objective function was  4.02 with Chi Square of  1980.33
The degrees of freedom for the model are 89  and the objective function was  0.16 

The root mean square of the residuals (RMSR) is  0.02 
The df corrected root mean square of the residuals is  0.03 

The harmonic number of observations is  500 with the empirical chi square  65.43  with prob <  0.97 
The total number of observations was  500  with Likelihood Chi Square =  79.04  with prob <  0.77 

Tucker Lewis Index of factoring reliability =  1.007
RMSEA index =  0  and the 90 % confidence intervals are  0 0.018
BIC =  -474.06
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy             
                                                   MR1  MR2
Correlation of (regression) scores with factors   0.91 0.90
Multiple R square of scores with factors          0.83 0.82
Minimum correlation of possible factor scores     0.66 0.63
Factor Analysis using method =  minres
Call: fa(r = sim.item(16), nfactors = 2)
Standardized loadings (pattern matrix) based upon correlation matrix
      MR2   MR1   h2   u2 com
V1   0.59 -0.01 0.35 0.65   1
V2   0.62  0.03 0.39 0.61   1
V3   0.58 -0.01 0.34 0.66   1
V4   0.63  0.04 0.40 0.60   1
V5   0.05 -0.66 0.44 0.56   1
V6  -0.06 -0.63 0.41 0.59   1
V7   0.01 -0.61 0.37 0.63   1
V8   0.03 -0.59 0.34 0.66   1
V9  -0.59  0.00 0.35 0.65   1
V10 -0.58  0.00 0.33 0.67   1
V11 -0.58  0.01 0.34 0.66   1
V12 -0.58  0.06 0.34 0.66   1
V13 -0.06  0.63 0.40 0.60   1
V14  0.00  0.58 0.34 0.66   1
V15  0.03  0.60 0.36 0.64   1
V16  0.07  0.60 0.38 0.62   1

                       MR2  MR1
SS loadings           2.84 3.02
Proportion Var        0.18 0.19
Cumulative Var        0.18 0.37
Proportion Explained  0.49 0.51
Cumulative Proportion 0.49 1.00

 With factor correlations of 
     MR2  MR1
MR2 1.00 0.06
MR1 0.06 1.00

Mean item complexity =  1
Test of the hypothesis that 2 factors are sufficient.

The degrees of freedom for the null model are  120  and the objective function was  4.02 with Chi Square of  1980.33
The degrees of freedom for the model are 89  and the objective function was  0.16 

The root mean square of the residuals (RMSR) is  0.02 
The df corrected root mean square of the residuals is  0.03 

The harmonic number of observations is  500 with the empirical chi square  65.43  with prob <  0.97 
The total number of observations was  500  with Likelihood Chi Square =  79.04  with prob <  0.77 

Tucker Lewis Index of factoring reliability =  1.007
RMSEA index =  0  and the 90 % confidence intervals are  0 0.018
BIC =  -474.06
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy             
                                                   MR2  MR1
Correlation of (regression) scores with factors   0.90 0.91
Multiple R square of scores with factors          0.82 0.83
Minimum correlation of possible factor scores     0.63 0.66

psych documentation built on June 19, 2021, 1:06 a.m.