Learn R Programming

growcurves (version 0.2.4.0)

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.

Usage

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

Arguments

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.

Value

A list object containing quantile summaries for all sampled model parameters.
deviance.summary
vector of length 3 summarizing quantiles for model deviance.
beta.summary
Nfixed x 3 quantile summaries of model fixed effects.
alpha.summary
quantile summary of model global intercept parameter.
bmat.summary
list object of length Nrandom, each cell containing a Nsubject x 3 matrix of by-subject parameter quantile summaries.
tauu.summary
Nmv 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.summary
quantile summary for model error precision parameter.
taub
Nrandom x 3 quantile summaries for subject effect precision parameters.
u.summary
S*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.summary
Nsubj.aff x 3 quantile summaries derived from multiplying the affected subject weight matrix by the multiple membership random effects.
M.summary
quantile summary for number of DP posterior clusters formed.
Dbar
Model fit statistics.
pD
Model fit statistics.
pV
Model fit statistics.
DIC
Model fit statistics.
lpml
Model fit statistics.

See Also

dpgrowmm, dpgrow