Package: mme 0.1-6

mme: Multinomial Mixed Effects Models

Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.

Authors:E. Lopez-Vizcaino, M.J. Lombardia and D. Morales

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

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On CRAN:

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

2.59 score 1 stars 39 scripts 167 downloads 30 exports 3 dependencies

Last updated 6 years agofrom:3acfdec03f. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKNov 13 2024
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R-4.5-linuxOKNov 13 2024
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Exports:addtolistaddtomatrixcidata.mmeFbetafFbetaf.ctFbetaf.itinitial.valuesmmedatamodelmodelfit1modelfit2modelfit3msebmsefmsef.ctmsef.itomegaphi.directphi.direct.ctphi.direct.itphi.multphi.mult.ctphi.mult.itprmuprmu.timesPhikfsPhikf.ctsPhikf.itwmatrix

Dependencies:latticeMASSMatrix

mme: tutorial for mme package

Rendered frommme_vignette.Rnwusingutils::Sweaveon Nov 13 2024.

Last update: 2013-06-10
Started: 2013-06-10

Readme and manuals

Help Manual

Help pageTopics
Multinomial Mixed Effects Modelsmme-package mme
Add items from a listaddtolist
Add rows from a matrixaddtomatrix
Standard deviation and p-values of the estimated model parametersci
Function to generate matrices and the initial valuesdata.mme
Inverse of the Fisher information matrix of the fixed and random effects in Model 1Fbetaf
Inverse of the Fisher information matrix of fixed and random effects in Model 3Fbetaf.ct
The inverse of the Fisher information matrix of the fixed and random effects for Model 2Fbetaf.it
Initial values for fitting algorithm to estimate the fixed and random effects and the variance componentsinitial.values
Create objects of class mmedatammedata
Choose between the three modelsmodel
Function used to fit Model 1modelfit1
Function to fit Model 2modelfit2
Function used to fit Model 3modelfit3
Bias and MSE using parametric bootstrapmseb
Analytic MSE for Model 1msef
Analytic MSE for Model 3msef.ct
Analytic MSE for Model 2msef.it
Model correlation matrix for Model 3omega
Variance components for Model 1phi.direct
Variance components for Model 3phi.direct.ct
Variance components for Model 2phi.direct.it
Initial values for the variance components for Model 1phi.mult
Initial values for the variance components in Model 3phi.mult.ct
Initial values for the variance components in Model 2phi.mult.it
Print objects of class mmeprint.mme
Estimated mean and probabilities for Model 1prmu
Estimated mean and probabilities for Model 2 and 3prmu.time
Dataset for Model 1simdata
Dataset for Model 2simdata2
Dataset for Model 3simdata3
Fisher information matrix and score vectors of the variance components for Model 1sPhikf
Fisher information matrix and score vectors of the variance components for Model 3sPhikf.ct
Fisher information matrix and score vectors of the variance components for Model 2sPhikf.it
Model variance-covariance matrix of the multinomial mixed modelswmatrix