Package: pleLMA 0.2.1

pleLMA: Pseudo-Likelihood Estimation of Log-Multiplicative Association Models

Log-multiplicative association models (LMA) are models for cross-classifications of categorical variables where interactions are represented by products of category scale values and an association parameter. Maximum likelihood estimation (MLE) fails for moderate to large numbers of categorical variables. The 'pleLMA' package overcomes this limitation of MLE by using pseudo-likelihood estimation to fit the models to small or large cross-classifications dichotomous or multi-category variables. Originally proposed by Besag (1974, <doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood estimation takes large complex models and breaks it down into smaller ones. Rather than maximizing the likelihood of the joint distribution of all the variables, a pseudo-likelihood function, which is the product likelihoods from conditional distributions, is maximized. LMA models can be derived from a number of different frameworks including (but not limited to) graphical models and uni-dimensional and multi-dimensional item response theory models. More details about the models and estimation can be found in the vignette.

Authors:Carolyn J. Anderson

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pleLMA.pdf |pleLMA.html
pleLMA/json (API)
NEWS

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

Peer review:

Datasets:
  • dass - Dateframe of responses to items from depression, anxiety, and stress scales
  • vocab - Dataframe of response to vocabulary items from the 2018 General Social Survey

On CRAN:

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

2.04 score 11 scripts 149 downloads 24 exports 26 dependencies

Last updated 3 years agofrom:506e1bcd6e. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 14 2024
R-4.5-winOKOct 14 2024
R-4.5-linuxOKOct 14 2024
R-4.4-winOKOct 14 2024
R-4.4-macOKOct 14 2024
R-4.3-winOKOct 14 2024
R-4.3-macOKOct 14 2024

Exports:convergence.statsconvergenceGPCMerror.checkfit.gpcmfit.independencefit.nominalfit.raschFitStackfitStackGPCMitem.gpcmItemDataItemGPCM.dataItemLoopiterationPlotlma.summaryple.lmareScaleItemScaleScaleGPCMscalingPlotset.upStackDataStackDataGPCMtheta.estimates

Dependencies:clidfidxdplyrfansiFormulagenericsgluelatticelifecyclelmtestmagrittrMASSmlogitpillarpkgconfigR6rbibutilsRdpackrlangstatmodtibbletidyselectutf8vctrswithrzoo

Pseudo-likelihood Estimation of Log-mulitplicative association models: The pleLMA Package

Rendered frompleLMA_vignette.Rmdusingknitr::rmarkdownon Oct 14 2024.

Last update: 2021-10-05
Started: 2021-04-27

Readme and manuals

Help Manual

Help pageTopics
Computes statistics to assess convergence of the nominal modelconvergence.stats
Computes statistics to assess convergence for generalized partial credit modelsconvergenceGPCM
Dateframe of responses to items from depression, anxiety, and stress scalesdass
Checks for basic errors in input to the 'ple.lma' functionerror.check
Fits LMA model where category scale values equal a_im * x_jfit.gpcm
Fits the log-linear model of independencefit.independence
Fits the nominal modelfit.nominal
Fits an LMA using fixed category scoresfit.rasch
Up-dates association parameters of the nominal modelFitStack
Up-dates association parameters of the GPCM by fitting model to stacked datafitStackGPCM
Estimates item parameters of LMA with linear restrictions on category scoresitem.gpcm
Prepares data for up-dating scale value parameters of nominal modelItemData
Creates data frame up-dating phi parameters of the gpcm.ItemGPCM.data
loops through items and up-dates estimates of scale values for each item in Nominal ModelItemLoop
Plots estimated parameters by iteration for the gpcm and nominal modelsiterationPlot
Produces a summary of resultslma.summary
Main function for estimating parameters of LMA modelsple.lma
Re-scales the category scale values and Phi after convergence of the nominal modelreScaleItem
Imposes scaling constraint to identify parameters of the LMA (nominal) modelScale
Imposes scaling constraint to identify parameters of LMA (GPCM)ScaleGPCM
Graphs estimated scale values by integers of the LMA (nominal) modelscalingPlot
Sets up the data based on input data and model specificationsset.up
Prepares data for up-dating association parameters of a multidimensional nominal LMAStackData
Prepares data for up-dating association parameters of LMA (GPCM) modelStackDataGPCM
Computes estimates of theta (values on latent trait(s)) for all LMA modelstheta.estimates
Dataframe of response to vocabulary items from the 2018 General Social Surveyvocab