EffectsEst.BBNR: Specific Newton-Raphson based algorithm for the estimation of...

In idaejin/PROreg: Patient Reported Outcomes Regression Analysis

Description

Specific Newton-Raphson based algorithm for the estimation of the fixed and random effects in a beta-binomial mixed effects model.

Usage

EffectsEst.BBNR(y,m,beta,u,p,phi,D.,X,Z,maxiter)

Arguments

y	dependent response variable in the model.
m	maximum score number in each beta-binomial observation.
beta	initial values of the fixed-effects.
u	initial values of the random-effects.
p	initial values of the probability parameter of the beta-binomial distribution.
phi	estimated value of the dispersion parameter of the beta-binomial distribution.
D.	estimated value of the variance-covariance matrix of the random effects.
X	model matrix of the fixed effects.
Z	model matrix of the random effects.
maxiter	maximum number of iterations for the algorithm.

Details

EffectsEst.BBNR function performs a Newton-Raphson based mamximum likelihood estimation of the fixed and random effects in a beta-binomial mixed-effects models given some initial values.

See 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 for more information.

Value

EffectsEst.BBNR returns a list of the estimates and variances of the fixed and random effects.

fixed.est	estimated value of the fixed coefficients of the regression.
random.est	estimated value of the random coefficients of the regression.
vcov.fixed	variance-covariance matrix of the estiamtion of the fixed-effects.
var.random	variance of the estimation of the random effects.
iter.fixrand	numbero of iterations in the algorithm.
conv.fixrand	convergence og the algorithm.

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, to appear in Biometrical Journal.

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