Employs a non-parametric formulation for by-subject random effect parameters to borrow strength over a constrained number of repeated measurement waves in a fashion that permits multiple effects per subject. One class of models employs a Dirichlet process (DP) prior for the subject random effects and includes an additional set of random effects that utilize a different grouping factor and are mapped back to clients through a multiple membership weight matrix; e.g. treatment(s) exposure or dosage. A second class of models employs a dependent DP (DDP) prior for the subject random effects that directly incorporates the multiple membership pattern.
updated release on CRAN.
performs Bayesian mixed effects modeling on repeated measures data.
allows a DP prior on a set of subject random effects to borrow strength across subjects for estimation.
simultaneously supports definition of random effects under other than subject groupings with one or more multiple membership (MM) terms.
the dpgrow function performs mixed effects modeling without an MM term (but with a DP prior on the set of subject random effects).
the dpgrowmm function extends dpgrow by allowing for a single MM term under one of three prior formulation options = c("mmi","mmigrp","mmcar").
the dpgrowmult function extends dpgrowmm by allowing for any number of MM terms, each under one of four prior formulation options = c("mmi","mmigrp","mmcar","mmdp").
a new ddpgrow function extends dpgrowmm and dpgrowmult by absorbing the MM term inside the subject effects such that each subject parameters their own MM effects.
-- prior formulation options = c("car","mvn","ind").
there are also 3 accompanying graphical accessor functions for the 3 sampling functions to promote easy analysis:
the growplot function produces and plots by-subject growth curves under any user defined grouping.
the trtplot function compares the distribution for the difference in fixed effects means between any two treatment arms.
the effectsplot function compares the mean effect values for an MM term under different prior and model formulations.
ustarj = ustar(s(j));