growcurves (version 0.2.3.7)

datsim: Repeated measures for two groups of subjects drawn from mmcar model with no nuisance covariates

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 areN = 792total measures forP = 264subjects
• subject. subject identifier(1,2,...,264
• trt. treatment group identifier of lengthN(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 lengthN. 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 theP_aff = 132affected subjects (insubj.aff) to any ofS = 245treatment sessions.
• group. treatment group membership for each of theSsessions.
• Omega. theS x SCAR adjacency matrix used to model prior dependence among sessions
• gamma. true session effect values (of lengthS) used to generate model response.
• s. true cluster memberships for each of thePsubjects.
• b.star. a list object of true cluster location values for each ofM = 10clusters. Each entry contains then.random = 3location values for that cluster.
• b. a list object true random effect coefficient values for each ofPsubjects. Each entry contains then.random = 3effect values for that subject.
• tau.b. true values for the prior precisions of the base Gaussian distribution for each ofn.random = 3subject effects.
• tau.e. true value for overall model error.
• coefs. true coefficient values for the time-based quadratic fixed effects generated from thetrt, andtimeinputs. e.g. X = c(1,time,time^2,trt_1,trt_1*time,trt_1*time^2).