summary_quantiles: Produce quantile summaries of model posterior samples

Description

Inputs MCMC samples for model parameters and constructs c(2.5%,50%,97.5%) quantile summaries.

summary_quantiles(model.output, Nfixed, Nrandom, Nsubject, Nsubj.aff = NULL,
  Nmv = 1, Nsession = NULL)

model.output

An output object of class within c("dpgrow", "dpgrowmm")

Nfixed

Number of total fixed effects, both time-based and nuisance.

Nrandom

Number of total random effects, both time-based and nuisance, all grouped by subject.

Nsubject

Number of unique subjects (on which repeated measures are observed).

Nsubj.aff

Number of subjects, P.aff, receiving multiple membership effects

Nmv

Number of multivariate MM effects. Defaults to 1 for univariate MM if left blank.

Nsession

Number of multiple membership effects for each entry in "Nmv". May be left blank for univariate MM.

A list object containing quantile summaries for all sampled model parameters.
deviance.summaryvector of length 3 summarizing quantiles for model deviance.
beta.summaryNfixed x 3 quantile summaries of model fixed effects.
alpha.summaryquantile summary of model global intercept parameter.
bmat.summarylist object of length Nrandom, each cell containing a Nsubject x 3 matrix of by-subject parameter quantile summaries.
tauu.summaryNmv x 3 quantile summary for prior precision parameters employed for multiple membership random effects. An nty x 3 matrix in the case of nty multiple membership effect terms.
taue.summaryquantile summary for model error precision parameter.
taubNrandom x 3 quantile summaries for subject effect precision parameters.
u.summaryS*Nmv x 3 quantile summaries for multiple membership random effect parameters. A list of such matrices in the case of nty multiple membership effect terms.
mm.summaryNsubj.aff x 3 quantile summaries derived from multiplying the affected subject weight matrix by the multiple membership random effects.
M.summaryquantile summary for number of DP posterior clusters formed.
DbarModel fit statistics.
pDModel fit statistics.
pVModel fit statistics.
DICModel fit statistics.
lpmlModel fit statistics.