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mme (version 0.1-6)

Multinomial Mixed Effects Models

Description

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) ; 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) .

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Version

Install

install.packages('mme')

Monthly Downloads

165

Version

0.1-6

License

GPL (>= 2)

Maintainer

E. Lopez-Vizcaino

Last Published

January 27th, 2019

Functions in mme (0.1-6)

Add rows from a matrix

Analytic MSE for Model 1

Estimated mean and probabilities for Model 1

Estimated mean and probabilities for Model 2 and 3

Inverse of the Fisher information matrix of the fixed and random effects in Model 1

Inverse of the Fisher information matrix of fixed and random effects in Model 3

Analytic MSE for Model 3

Analytic MSE for Model 2

Fisher information matrix and score vectors of the variance components for Model 1

Fisher information matrix and score vectors of the variance components for Model 3

The inverse of the Fisher information matrix of the fixed and random effects for Model 2

Add items from a list

Variance components for Model 3

Variance components for Model 2

Multinomial Mixed Effects Models

Create objects of class mmedata

Choose between the three models

Function used to fit Model 1

Initial values for the variance components in Model 2

Print objects of class mme

Model variance-covariance matrix of the multinomial mixed models

Model correlation matrix for Model 3

Variance components for Model 1

Initial values for the variance components for Model 1

Initial values for the variance components in Model 3

Function to generate matrices and the initial values

Initial values for fitting algorithm to estimate the fixed and random effects and the variance components

Function to fit Model 2

Dataset for Model 2

Dataset for Model 3

Function used to fit Model 3

Fisher information matrix and score vectors of the variance components for Model 2

Dataset for Model 1

Standard deviation and p-values of the estimated model parameters

Bias and MSE using parametric bootstrap