faMAP: Velicer's minimum partial correlation method for determining...

In fungible: Psychometric Functions from the Waller Lab

Velicer's minimum partial correlation method for determining the number of major components for a principal components analysis or a factor analysis

Description

Uses Velicer's MAP (i.e., matrix of partial correlations) procedure to determine the number of components from a matrix of partial correlations.

Usage

faMAP(R, max.fac = 8, Print = TRUE, Plot = TRUE, ...)

Arguments

R	input data in the form of a correlation matrix.
max.fac	maximum number of dimensions to extract.
Print	(logical) Print = TRUE will print complete results.
Plot	(logical) Plot = TRUE will plot the MAP values.
...	Arguments to be passed to the plot functions (see par).

Value

MAP	Minimum partial correlations
MAP4	Minimum partial correlations
fm	average of the squared partial correlations after the first m components are partialed out.
fm4	see Velicer, Eaton, & Fava, 2000.
PlotAvgSq	A saved object of the original MAP plot (based on the average squared partial r's.)
PlotAvg4th	A saved object of the revised MAP plot (based on the average 4th power of the partial r's.)

Author(s)

Niels Waller

References

Velicer, W. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3):321–327.

Velicer,W. F., Eaton, C. A. , & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.). Problems and Solutions in Human Assessment: Honoring Douglas N. Jackson at Seventy (pp. 41-71. Boston, MA: Kluwer Academic.

Examples

	# Harman's data (1967, p 80) 
	# R = matrix(c(
	# 1.000,  .846,  .805,  .859,  .473,  .398,  .301,  .382,
	#  .846, 1.000,  .881,  .826,  .376,  .326,  .277,  .415,
	#  .805,  .881, 1.000,  .801,  .380,  .319,  .237,  .345,
	#  .859,  .826,  .801, 1.000,  .436,  .329,  .327,  .365,
	#  .473,  .376,  .380,  .436, 1.000,  .762,  .730,  .629,
	#  .398,  .326,  .319,  .329,  .762, 1.000,  .583,  .577,
	#  .301,  .277,  .237,  .327,  .730,  .583, 1.000,  .539,
	#  .382,  .415,  .345,  .365,  .629,  .577,  .539, 1.000), 8,8)

	  F <- matrix(c(  .4,  .1,  .0,
	                  .5,  .0,  .1,
	                  .6,  .03, .1,
	                  .4, -.2,  .0,
	                   0,  .6,  .1,
	                  .1,  .7,  .2,
	                  .3,  .7,  .1,
	                   0,  .4,  .1,
	                   0,   0,  .5,
	                  .1, -.2,  .6, 
	                  .1,  .2,  .7,
	                 -.2,  .1,  .7),12,3)
					 
	  R <- F %*% t(F)
	  diag(R) <- 1 
	  
  	faMAP(R, max.fac = 8, Print = TRUE, Plot = TRUE) 

fungible documentation built on March 31, 2023, 5:47 p.m.