dqmix

A B by M matrix, containing the probability components of the infinite mixture of beta distribution.
The output of <code><a rd-options="" href="/link/lmeNBBayes?package=lmeNBBayes&version=1.3.1" data-mini-rdoc="lmeNBBayes::lmeNBBayes">lmeNBBayes</a></code>.

weightH1

A B by M matrix, containing the shape1 parameters of the infinite mixture of beta distribution.
The output of <code><a rd-options="" href="/link/lmeNBBayes?package=lmeNBBayes&version=1.3.1" data-mini-rdoc="lmeNBBayes::lmeNBBayes">lmeNBBayes</a></code>.

A B by M matrix, containing the shape2 parameters of the infinite mixture of beta distribution.
The output of <code><a rd-options="" href="/link/lmeNBBayes?package=lmeNBBayes&version=1.3.1" data-mini-rdoc="lmeNBBayes::lmeNBBayes">lmeNBBayes</a></code>.


The grids of points at which the density is evaluated. Must be the points within 0 and 1.


alphas


If TRUE, the density estimate of the random effect is return, if FALSE then the quantiles of the random effects are estimated.


dens

Given the output of the <code><a rd-options="" href="/link/lmeNBBayes?package=lmeNBBayes&version=1.3.1" data-mini-rdoc="lmeNBBayes::lmeNBBayes">lmeNBBayes</a></code>, this function estimate the posterior density of the random effect at grids.

 ~kwd1 

 ~kwd2 

The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.

dqmix: Estimate the random effect distribution of the flexible mixed effect negative binomial regression.

Description

Usage

Arguments

References

See Also

Examples