Package: fungible 2.4.4

fungible: Psychometric Functions from the Waller Lab

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <doi:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <doi:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <doi:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <doi:10.1007/s11336-017-9599-0>.

Authors:Niels Waller [aut, cre], Justin Kracht [ctb], Jeff Jones [ctb], Casey Giordano [ctb], Hoang V. Nguyen [ctb]

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# Install 'fungible' in R:
install.packages('fungible', repos = c('https://nwaller.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ACL - Adjective Checklist Data.
  • AmzBoxes - Length, width, and height measurements for 98 Amazon shipping boxes
  • BadRBY - Improper correlation matrix reported by Bentler and Yuan
  • BadRJN - Improper R matrix reported by Joseph and Newman
  • BadRKtB - Improper R matrix reported by Knol and ten Berge
  • BadRLG - Improper R matrix reported by Lurie and Goldberg
  • BadRRM - Improper R matrix reported by Rousseeuw and Molenberghs
  • Boruch70 - Multi-Trait Multi-Method correlation matrix reported by Boruch, Larkin, Wolins, and MacKinney
  • Box20 - Length, width, and height measurements for Thurstone's 20 boxes
  • Box26 - R matrix for Thurstone's 26 hypothetical box attributes.
  • HS9Var - 9 Variables from the Holzinger and Swineford (1939) Dataset
  • HW - Six data sets that yield a Heywood case
  • Jackson67 - Multi-Trait Multi-Method correlation matrix reported by Jackson and Singer
  • Malmi79 - Multi-Trait Multi-Method correlation matrix reported by Malmi, Underwood, and Carroll (1979).
  • Thurstone41 - Multi-Trait Multi-Method correlation matrix reported by Thurstone and Thurstone (1941).
  • ThurstoneBox20 - Factor Pattern and Factor Correlations for Thurstone's 20 hypothetical box attributes.
  • ThurstoneBox26 - Factor Pattern Matrix for Thurstone's 26 box attributes.

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5.46 score 8 packages 138 scripts 2.9k downloads 98 exports 63 dependencies

Last updated 9 months agofrom:bd9f2af1b1. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 05 2024
R-4.5-winNOTENov 05 2024
R-4.5-linuxNOTENov 05 2024
R-4.4-winOKNov 05 2024
R-4.4-macOKNov 05 2024
R-4.3-winOKNov 05 2024
R-4.3-macOKNov 05 2024

Exports:adfCoradfCovalphaRBiFADbigencbcfiCompleteRcvxCompleteRdevCompleteRmapcorDensitycorSamplecorSmoothcosMatd2reapeigGenenhancementerffaAlignfaBoundsfaEKCfaIBfaLocalMinfalsfaMainfaMAPfaMBfapafaregfaScoresfaSortfaStandardizefaXFMPFMPMonotonicityCheckfsIndeterminacyfungiblefungibleExtremafungibleLfungibleRFUPgen4PMDatagenCorrGenerateBoxDatagenFMPDatagenPhiget_wb_modirfitemDescriptiveskurtLedermannmontemonte1noisemakernormalCornormFobj_funcOmegaorderFactorspromaxQr2drarcRavgrRboundsrconercorrellipsoidrestScoreRGenrGivensrMAPrmsdrmseaRnpdMAPrPCASchmidLeimanseBetaseBetaCorseBetaFixedsemifysimFAskewSLismoothAPAsmoothBYsmoothKBsmoothLGsvdNormTaylorRusselltetcortetcorQuasitklTRvcosvnormVolElliptopewb

Dependencies:abindarmBHbitbit64bootclarabelcliclueclustercodacodetoolscrayonCVXRDEoptimdigestECOSolveRforeachGAgluegmpGPArotationiteratorslatticelavaanlifecyclelme4MASSMatrixMatrixModelsMBESSmcmcMCMCpackmiminqamnormtmvtnormnleqslvnlmenloptrnumDerivOpenMxosqppbivnormpbmcapplyquadprogquantregR6RcppRcppArmadilloRcppEigenRcppParallelRcsdprlangRmpfrrpfRSpectrascssemsemToolsSparseMStanHeaderssurvival

Simulating Population Correlation Matrices with Model Error

Rendered fromsimulate-model-error.Rmdusingknitr::rmarkdownon Nov 05 2024.

Last update: 2024-01-24
Started: 2024-01-24

Readme and manuals

Help Manual

Help pageTopics
Adjective Checklist Data.ACL
Asymptotic Distribution-Free Covariance Matrix of CorrelationsadfCor
Asymptotic Distribution-Free Covariance Matrix of CovariancesadfCov
Generate random R matrices with a known coefficient alphaalphaR
Length, width, and height measurements for 98 Amazon shipping boxesAmzBoxes
Improper correlation matrix reported by Bentler and YuanBadRBY
Improper R matrix reported by Joseph and NewmanBadRJN
Improper R matrix reported by Knol and ten BergeBadRKtB
Improper R matrix reported by Lurie and GoldbergBadRLG
Improper R matrix reported by Rousseeuw and MolenberghsBadRRM
Bifactor Analysis via Direct Schmid-Leiman (DSL) TransformationsBiFAD
Generate Correlated Binary Databigen
Multi-Trait Multi-Method correlation matrix reported by Boruch, Larkin, Wolins, and MacKinney (1970)Boruch70
Length, width, and height measurements for Thurstone's 20 boxesBox20
R matrix for Thurstone's 26 hypothetical box attributes.Box26
Cudeck & Browne (1992) model error methodcb
Calculate CFI for two correlation matricescfi
Complete a Partially Specified Correlation Matrix by Convex OptimizationCompleteRcvx
Complete a Partially Specified Correlation Matrix by the Method of Differential EvolutionCompleteRdev
Complete a Partially Specified Correlation Matrix by the Method of Alternating ProjectionsCompleteRmap
Generate the marginal density of a correlation from a uniformly sampled R matrix.corDensity
Sample Correlation Matrices from a Population Correlation MatrixcorSample
Smooth a Non PD Correlation MatrixcorSmooth
Compute the cosine(s) between either 2 matrices or 2 vectors.cosMat
Convert Degrees to Radiansd2r
Compute eap trait estimates for FMP and FUP modelseap
Generate eigenvalues for R matrices with underlying component structureeigGen
Find OLS Regression Coefficients that Exhibit Enhancementenhancement
Utility fnc to compute the components for an empirical response functionerf
Align the columns of two factor loading matricesfaAlign
Bounds on the Correlation Between an External Variable and a Common FactorfaBounds
Calculate Reference Eigenvalues for the Empirical Kaiser CriterionfaEKC
Inter-Battery Factor Analysis by the Method of Maximum LikelihoodfaIB
Investigate local minima in faMain objectsfaLocalMin
Unweighted least squares factor analysisfals
Automatic Factor Rotation from Random Configurations with Bootstrap Standard ErrorsfaMain
Velicer's minimum partial correlation method for determining the number of major components for a principal components analysis or a factor analysisfaMAP
Multiple Battery Factor Analysis by Maximum Likelihood MethodsfaMB
Iterated Principal Axis Factor Analysis (fapa)fapa
Regularized Factor Analysisfareg
Factor ScoresfaScores
Sort a factor loadings matrixfaSort
Standardize the Unrotated Factor LoadingsfaStandardize
Factor Extraction (faX) RoutinesfaX
Estimate the coefficients of a filtered monotonic polynomial IRT modelFMP
Utility function for checking FMP monotonicityFMPMonotonicityCheck
Understanding Factor Score Indeterminacy with Finite Dimensional Vector SpacesfsIndeterminacy
Generate Fungible Regression Weightsfungible
Locate Extrema of Fungible Regression WeightsfungibleExtrema
Generate Fungible Logistic Regression WeightsfungibleL
Generate Fungible Correlation MatricesfungibleR
Estimate the coefficients of a filtered unconstrained polynomial IRT modelFUP
Generate item response data for 1, 2, 3, or 4-parameter IRT modelsgen4PMData
Generate Correlation Matrices with User-Defined EigenvaluesgenCorr
Generate Thurstone's Box Data From length, width, and height box measurementsGenerateBoxData
Generate item response data for a filtered monotonic polynomial IRT modelgenFMPData
Create a random Phi matrix with maximum factor correlationgenPhi
Find an `lm` model to use with the Wu & Browne (2015) model error methodget_wb_mod
9 Variables from the Holzinger and Swineford (1939) DatasetHS9Var
Six data sets that yield a Heywood caseHW
Plot item response functions for polynomial IRT models.irf
Compute basic descriptives for binary-item analysisitemDescriptives
Multi-Trait Multi-Method correlation matrix reported by Jackson and Singer (1967)Jackson67
Calculate Univariate Kurtosis for a Vector or Matrixkurt
Ledermann's inequality for factor solution identificationLedermann
Multi-Trait Multi-Method correlation matrix reported by Malmi, Underwood, and Carroll (1979).Malmi79
Simulate Clustered Data with User-Defined Propertiesmonte
Simulate Multivariate Non-normal Data by Vale & Maurelli (1983) Methodmonte1
Simulate a population correlation matrix with model errornoisemaker
Compute Normal-Theory Covariances for CorrelationsnormalCor
Compute the Frobenius norm of a matrixnormF
Objective function for optimizing RMSEA and CFIobj_func
Compute Omega hierarchicalOmega
Order factor-loadings matrix by the sum of squared factor loadingsorderFactors
Plot Method for Class Monteplot.monte
Print Method for an Object of Class faMainprint.faMain
Print Method for an Object of Class faMBprint.faMB
Conduct an Oblique Promax RotationpromaxQ
Convert Radians to Degreesr2d
Rotate Points on the Surface on an N-Dimensional Ellipsoidrarc
Generate a random R matrix with an average rijRavgr
Generate random R matrices with user-defined bounds on the correlation coefficients via differential evolution (DE).Rbounds
Generate a Cone of Regression Coefficient Vectorsrcone
Generate Random PSD Correlation Matricesrcor
Generate Uniformly Spaced OLS Regression Coefficients that Yield a User-Supplied R-Squared Valuerellipsoid
Plot an ERF using rest scoresrestScore
Generate random R matrices with various user-defined properties via differential evolution (DE).RGen
Generate Correlation Matrices with Specified EigenvaluesrGivens
Generate Correlation Matrices with Specified EigenvaluesrMAP
Root Mean Squared Deviation of (A - B)rmsd
Calculate RMSEA between two correlation matricesrmsea
Generate Random NPD R matrices from a user-supplied population RRnpdMAP
Generate a Correlation Matrix from a Truncated PCA Loadings Matrix.rPCA
Schmid-Leiman Orthogonalization to a (Rank-Deficient) Bifactor StructureSchmidLeiman
Standard Errors and CIs for Standardized Regression CoefficientsseBeta
Standard Errors and CIs for Standardized Regression Coefficients from CorrelationsseBetaCor
Covariance Matrix and Standard Errors for Standardized Regression Coefficients for Fixed PredictorsseBetaFixed
Generate an sem model from a simFA model objectsemify
Generate Factor Analysis Models and Data Sets for Simulation StudiessimFA
Calculate Univariate Skewness for a Vector or Matrixskew
Conduct a Schmid-Leiman Iterated (SLi) Target RotationSLi
Smooth a NPD R matrix to PD using the Alternating Projection AlgorithmsmoothAPA
Smooth an NPD R matrix to PD using the Bentler Yuan 2011 methodsmoothBY
Smooth a Non PD Correlation Matrix using the Knol-Berger algorithmsmoothKB
Smooth NPD to Nearest PSD or PD MatrixsmoothLG
Summary Method for an Object of Class faMainsummary.faMain
Summary Method for an Object of Class faMBsummary.faMB
Summary Method for an Object of Class Monteprint.summary.monte summary.monte
Summary Method for an Object of Class Monte1print.summary.monte1 summary.monte1
Compute theta surrogates via normalized SVD scoressvdNorm
A generalized (multiple predictor) Taylor-Russell function.TaylorRussell
Compute ML Tetrachoric Correlationstetcor
Correlation between a Naturally and an Artificially Dichotomized VariabletetcorQuasi
Multi-Trait Multi-Method correlation matrix reported by Thurstone and Thurstone (1941).Thurstone41
Factor Pattern and Factor Correlations for Thurstone's 20 hypothetical box attributes.ThurstoneBox20
Factor Pattern Matrix for Thurstone's 26 box attributes.ThurstoneBox26
Optimize TKL parameters to find a solution with target RMSEA and CFI valuestkl
Estimate the parameters of the Taylor-Russell function.TR
Compute the Cosine Between Two Vectorsvcos
Norm a Vector to Unit Lengthvnorm
Compute the volume of the elliptope of possible correlation matrices of a given dimension.VolElliptope
Wu & Browne model error methodwb