Multinomial Mixed Effects Models
Add items from a list
Add rows from a matrix
Standard deviation and p-values of the estimated model parameters
Function to generate matrices and the initial values
Inverse of the Fisher information matrix of fixed and random effects i...
The inverse of the Fisher information matrix of the fixed and random e...
Inverse of the Fisher information matrix of the fixed and random effec...
Initial values for fitting algorithm to estimate the fixed and random ...
Multinomial Mixed Effects Models
Create objects of class mmedata
Choose between the three models
Function used to fit Model 1
Function to fit Model 2
Function used to fit Model 3
Bias and MSE using parametric bootstrap
Analytic MSE for Model 3
Analytic MSE for Model 2
Analytic MSE for Model 1
Model correlation matrix for Model 3
Variance components for Model 3
Variance components for Model 2
Variance components for Model 1
Initial values for the variance components in Model 3
Initial values for the variance components in Model 2
Initial values for the variance components for Model 1
Print objects of class mme
Estimated mean and probabilities for Model 1
Estimated mean and probabilities for Model 2 and 3
Fisher information matrix and score vectors of the variance components...
Fisher information matrix and score vectors of the variance components...
Fisher information matrix and score vectors of the variance components...
Model variance-covariance matrix of the multinomial mixed 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>.