# faLocalMin: Investigate local minima in faMain objects In fungible: Psychometric Functions from the Waller Lab

 faLocalMin R Documentation

## Investigate local minima in faMain objects

### 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

``````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.

• rmsdTable: (Matrix) A table of `RMSD` values for each pair of rotated factor patterns in solution set `Set`.

• Set: (Integer) The index of the user-specified solution set.

• complexity.val (Numeric): The common complexity value for all members in the user-specified solution set.

• HPcount: (Integer) The hyperplane count for each factor pattern in the solution set.

### Author(s)

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()`

### Examples

``````## Not run:
## Generate Population Model and Monte Carlo Samples ####
sout <- simFA(Model = list(NFac = 5,
NItemPerFac = 5,
Model = "orthogonal"),
MonteCarlo = list(NSamples = 100,
SampleSize = 500),
Seed = 655342)

## Population Phi matrix
sout\$Phi

## Compute EFA on Sample 67 ####
fout <- faMain (R = sout\$Monte\$MCData[[67]],
numFactors = 5,
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 March 31, 2023, 5:47 p.m.