datsim: Repeated measures for two groups of subjects drawn from mmcar...

In growcurves: Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects

Description

A simulation dataset containing repeated subject measures for 2 treatment groups, (control = 0, treatment = 1), constructed from an mmcar model with correlation between adjacent sessions equal to 0.25. Subject effects were randomly drawn from 10 clusters with weights/probabilities drawn from a Dirichlet distribution. Cluster location values were generated from a Gaussian base distribution.

Usage

datsim

Format

A list object of 19 variables for 792 total observations on 264 subjects

Details

• y. response. there are N = 792 total measures for P = 264 subjects

• subject. subject identifier (1,2,...,264

• trt. treatment group identifier of length N (e.g. (0,0,0,...,1,1,1,...) , either {0,1} for control and treatment.

• time. times in months for each repeated subject measure of length N. There are 3 distinct time points. e.g. (0,3,6,0,3,6,0,0,3,,,,)

• n.random. number of random effects per subject. Set = 3.

• n.fix_degree. order of fixed effects. Set = 2, for quadratic, meaning 3 effects (intercept, slope, quadratic) each, for treatment and control groups.

• coefs. true fixed effect coefficient values used to generate data.

• subj.aff. indexes subjects receiving treatment.

• W.subj.aff. multiple membership weight matrix that maps the P_aff = 132 affected subjects (in subj.aff) to any of S = 245 treatment sessions.

• group. treatment group membership for each of the S sessions.

• Omega. the S x S CAR adjacency matrix used to model prior dependence among sessions

• gamma. true session effect values (of length S) used to generate model response.

• s. true cluster memberships for each of the P subjects.

• b.star. a list object of true cluster location values for each of M = 10 clusters. Each entry contains the n.random = 3 location values for that cluster.

• b. a list object true random effect coefficient values for each of P subjects. Each entry contains the n.random = 3 effect values for that subject.

• tau.b. true values for the prior precisions of the base Gaussian distribution for each of n.random = 3 subject effects.

• tau.e. true value for overall model error.

• coefs. true coefficient values for the time-based quadratic fixed effects generated from the trt, and time inputs. e.g. X = c(1,time,time^2,trt_1,trt_1*time,trt_1*time^2).

growcurves documentation built on May 2, 2019, 7:03 a.m.