faLocalMin: Investigate local minima in faMain objects

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

View source: R/faLocalMin.R

Description

Compute pairwise root mean squared deviations (RMSD) among rotated factor patterns in an faMain object. Prior to computing the RMSD values, each pair of solutions is aligned to the first member of the pair. Alignment is accomplished using the Hungarian algorithm as described in faAlign.

Usage

1
faLocalMin(fout, Set = 1, HPthreshold = 0.1, digits = 5, PrintLevel = 1)

Arguments

fout

(Object from class faMain).

Set

(Integer) The index of the solution set (i.e., the collection of rotated factor patterns with a common complexity value) from an faMain object.

HPthreshold

(Scalar) A number between [0, 1] that defines the hyperplane threshold. Factor pattern elements below HPthreshold in absolute value are counted in the hyperplane count.

digits

(Integer) Specifies the number of significant digits in the printed output. Default digits = 5.

PrintLevel

(Integer) Determines the level of printed output. PrintLevel =

  • 0: No output is printed.

  • 1: Print output for the six most discrepant pairs of rotated factor patterns.

  • 2: Print output for all pairs of rotated factor patterns.

Details

Compute pairwise RMSD values among rotated factor patterns from an faMain object.

Value

faLocalMin function will produce the following output.

Author(s)

Niels Waller

See Also

Other Factor Analysis Routines: BiFAD(), Box26, GenerateBoxData(), Ledermann(), SLi(), SchmidLeiman(), faAlign(), faEKC(), faIB(), faMB(), faMain(), faScores(), faSort(), faStandardize(), faX(), fals(), fapa(), fareg(), fsIndeterminacy(), orderFactors(), print.faMB(), print.faMain(), promaxQ(), summary.faMB(), summary.faMain()

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
## Not run: 
  ## Generate Population Model and Monte Carlo Samples ####
  sout <- simFA(Model = list(NFac = 5,
                          NItemPerFac = 5,
                           Model = "orthogonal"),
              Loadings = list(FacLoadDist = "fixed",
                              FacLoadRange = .8),
              MonteCarlo = list(NSamples = 100, 
                                SampleSize = 500),
              Seed = 655342)

  ## Population EFA loadings
  (True_A <- sout$loadings)

  ## Population Phi matrix
  sout$Phi

  ## Compute EFA on Sample 67 ####
  fout <- faMain (R = sout$Monte$MCData[[67]],
                numFactors = 5,
                targetMatrix = sout$loadings,
                facMethod = "fals",
                rotate= "cfT",
                rotateControl = list(numberStarts = 50,
                                     standardize="CM",
                                     kappa = 1/25),
                Seed=3366805)

  ## Summarize output from faMain
  summary(fout, Set = 1, DiagnosticsLevel = 2, digits=4)

  ## Investigate Local Solutions
  LMout <- faLocalMin(fout, 
                    Set = 1,
                    HPthreshold = .15,
                    digits= 5, 
                    PrintLevel = 1)
                    
  ## Print hyperplane count for each factor pattern 
  ## in the solution set
  LMout$HPcount
  
## End(Not run)

fungible documentation built on Sept. 29, 2021, 1:06 a.m.