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lmeNB (version 1.2)

Fit negative binomial mixed-effect regression model.

Description

Fit negative binomial mixed-effect regression model. For the distribution of subject-specific random intercept, a gamma or log-normal distributions or semi-parametric procedure are allowed. For the safety monitoring, subject-specific conditional probability index can be computed. For details, see Zhao, Y., Li, D.K.B., Petkau, J.A., Riddehough, A. & Traboulsee, A. Detection of unusual increases in MRI lesion counts in multiple sclerosis patients.

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Version

Install

install.packages('lmeNB')

Monthly Downloads

32

Version

1.2

License

GPL (>= 2)

Maintainer

Yumi Kondo

Last Published

January 24th, 2014

Functions in lmeNB (1.2)

Fit the semi-parametric negative binomial mixed-effect independent model.

Performs the maximum likelihood estimation for the negative binomial mixed-effect AR(1) model

Fit the semi-parametric negative binomial mixed-effect AR(1) model.

Performs the maximum likelihood estimation for the negative binomial mixed-effect model. This function is a wrapper for mle.fun, mle.ar1.fun, mle.a3.fun and mle.ar1.non3.

Compute a conditional probability of observing a set of counts as extreme as the new observations of a subjectvisit given the previous observations of the same subject based on the negative binomial mixed-effect AR(1) model.

Simulate a dataset from the negative binomial mixed-effect independent/AR(1) model

Performs the maximum likelihood estimation for the negative binomial mixed-effect independent model

Compute the point estimate and its 95of the conditional probability Pr(q(Y_i,new)>=q(y_i,new)| Y_i,pre=y_i,pre)

Compute a conditional probability of observing a set of counts as extreme as the new observations of a subject given the previous observations from the same subject based on the negative binomial mixed effect independent model.

Internal lmeNB functions