VarEst: Estimation of the dispersion parameters in a beta-binomial...

In idaejin/PROreg: Patient Reported Outcomes Regression Analysis

Description

VarEst function performs a mamximum likelihood estimation of the dispersion parameters in a beta-binomial mixed-effects models given some initial values.

Usage

VarEst(y,m,p,X,Z,u,nRand,nComp,nRandComp,OLDall.sigma,OLDphi,q)

Arguments

y	dependent response variable in the model.
m	maximum score number in each beta-binomial observation.
p	estimated values of the probability parameter.
X	model matrix of the fixed effects.
Z	model matrix of the random effects.
u	estimated values of the random-effects.
nRand	number of random effects in the model.
nComp	number of random effects components, i.e., number of different random effects.
nRandComp	the number of random effects in each random component of the model. It must be especified as a vector, where the 'i'th value must correspond with the number of random effects of the 'i'th random component
OLDall.sigma	initial values of the variance parameters of the random effects.
OLDphi	initial value of the dispersion parameter of the beta-binomial distribution.
q	number of independent variables or covariates in the fixed part of the model.

Details

VarEst function performs a mamximum likelihood estimation of the dispersion parameters in a beta-binomial mixed-effects models given some initial values.

It uses the multiroot and uniroot functions of the rootSolve R-package.

Value

VarEst returns a list of estimates and variance of the dispersion parameters.

(phi=phi,all.sigma=all.sigma,psi=psi,psi.var=psi.var,all.sigma.var=sigma.var)

phi	estimated value of the dispersion parameter in the beta-binomial distribution.
all.sigma	estimated value of the variances of the random effects.
psi	estimated value of the logarithm of the dispersion parameter in the beta-binomial distribution.
psi.var	variance of the estimated value of the logarithm of the dispersion parameter in the beta-binomial distribution.
all.sigma.var	variance of the estimated value of the variances of the random effects.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Breslow N. E. & Calyton D. G. (1993): Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 9-25

Lee Y. & Nelder J. A. (1996): Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B, 58, 619-678

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2018): A beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes, Biometrical Journal.

See Also

The multiroot and uniroot functions of the R-package rootSolve for the general Newton-Raphson algorithm.

idaejin/PROreg documentation built on May 9, 2019, 5:04 a.m.