faLocalMin  R Documentation 
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
.
faLocalMin(fout, Set = 1, HPthreshold = 0.1, digits = 5, PrintLevel = 1)
fout 
(Object from class 
Set 
(Integer) The index of the solution set (i.e., the collection of
rotated factor patterns with a common complexity value) from an

HPthreshold 
(Scalar) A number between [0, 1] that defines the
hyperplane threshold. Factor pattern elements below 
digits 
(Integer) Specifies the number of significant
digits in the printed output. Default 
PrintLevel 
(Integer) Determines the level of printed output. PrintLevel =

Compute pairwise RMSD values among rotated factor patterns from
an faMain
object.
faLocalMin
function will produce the following output.
rmsdTable: (Matrix) A table of RMSD
values for each pair of
rotated factor patterns in solution set Set
.
Set: (Integer) The index of the userspecified solution set.
complexity.val (Numeric): The common complexity value for all members in the userspecified solution set.
HPcount: (Integer) The hyperplane count for each factor pattern in the solution set.
Niels Waller
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()
## 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)
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