ddpPost: Run a Bayesian mixed effects model for by-subject random...
In growcurves: Bayesian Semi and Nonparametric Growth Curve Models that Additionally Include Multiple Membership Random Effects

Description Usage Arguments Value Note Author(s) See Also

An internal function to ddpgrow

1 2	ddpPost(y, X, Z, subject, dosemat, numt, typet, Omega, omegaplus, n.iter, n.burn, n.thin, shapealph, ratebeta, M.init)

`y`	An N x 1 response (of subject-measure cases)
`X`	Fixed effects design matrix
`Z`	Random effects design matrix. Assumed grouped by `subjects`
`subject`	An N x 1 set of subject identifiers
`dosemat`	An P x T Anova or Multiple Membership design matrix linking treatment dosages to subjects where T is the total number dosages across all treatments + 1 for an intercept. This formulation assumes there is a hold-out dose for each treatment. e.g. the null dosage.
`numt`	A numeric vector of length equal to the number of treatments that contains the number of dosages for each treatment.
`typet`	A numeric vector of length equal to the number of treatments that contains the base distribution for each treatment. `1 = "car"`, `2 = "mvn"`, `3 = "ind"`
`Omega`	A list object of length equal to the number of treatments with `"car"` selected for base distribution. Each entry is an `numt[m] x numt[m]` numeric CAR adjacency matrix for the dosages of treatment `m`.
`omegaplus`	A list object of length equal to the number of treatments under `"car"` containing numeric vectors that are rowSums of the corresponding matrix element in `Omega`.
`n.iter`	The number of MCMC iterations
`n.burn`	The number of MCMC burn-in iterations to discard
`n.thin`	The step increment of MCMC samples to return
`shapealph`	The shape parameter for the Γ prior on the DP concentration parameter.
`ratebeta`	The rate parameter for the Γ prior on the DP concentration parameter.
`M.init`	Initial MCMC chain scalar value for number of by-subject clusters. If excluded defaults to `length(unique(subject))`.