1 multiple membership random effects (block) term.q, to be equal
to the number of measurement waves, T. Random
effects are grouped by subject and all q
parameters receive the DP prior. Additional sets of
possibly more than 1 multiple membership effect
terms are included, each with a separate weight/design
matrix that maps the effects back to clients. A variety
of prior formulations are available for the effects in
each multiple membership term.dpgrowmult(y, subject, trt, time, n.random, n.fix_degree,
formula, random.only, data, Omega, group, subj.aff,
W.subj.aff, n.iter, n.burn, n.thin, strength.mm,
shape.dp, rate.dp, plot.out, option, ulabs)N captures
the number of subject-time cases (repeated subject
measures). Data may reflect unequal number of measures
per subject. Missing o(1,1,1,2,2,3,3,3,...N (number of cases) indicating treatment group
assignments for each case. May also be input as length
P vector, where P is the number of unique
subjects, indicatiN,
capturing the time points associated to each by-subject
measure. Mav leave blank if only one time point (no
repeated measures).q. Since a DP prior is used on
client effects, may be set equal to the number of
measurement waves, T. The y, trt, time
vectors will together be used t(time, ...,
time^(n.fix_degree), trt_1,time*trt_1, ...
,time^(n.fix_degree)*trt_l, trt_L,...,
time^(nformula with the following format,
y ~ x_1 + x_2*x_3 | z_1*z_2 as an object of class
formula. The bar, |, separates fixed and
random effecformula is entered without a |,
random.only defaultsdata.frame containing the variables
with names as specified in formula, including the
response, y."mmcar" option. List element i contains an
S[i] x S[i] numeric matrix to encode the CAR
adjacency matrix, "mmcar" or "mmigrp". List element i
contains a numeric or character vector of length
S[i], provii contains
a Paff[i] x 1 vector subset of subject
composed with unique subject identifiers that are linked
to the effects in tei contains a
P.aff[i] x S[i] numeric matrix that maps a set of
random effects to affected subjects
(subj.aff[[i]]). It is assumed that tdpgrow will return (n.iter - n.burn)
posterior samples.tau_{gamma} ~ G(strength.mm,strength.mm)
prior on the precision parameter of either a CAR
(gamma ~ CAR(tau_gamma)) or independent
(gamma ~ N(0,tau_gamma^(-1)I_S) priorn is the
total number of subjects.TRUE.option are confined to choose from among
c("mmcar","mmi","mmigrp"option
containing desired labels for each multiple membership
term. These label values are employed in returned plot
objects. If left blank, ulabs is set to a
sequential number vector dpgrowmult object, for which many methods are
available to return and view results. Generic functions
applied to an object, res of class dpgrow,
includes:call, the
function call made to dpgrowmult and
summary.results, which contains a list of objects
that include 95% credible intervals for each set
of sampled parameters, specified as (2.5%, mean,
97.5%, including fixed and random effects. Also
contains model fit statistics, including DIC (and
associated Dbar, Dhat, pD,
pV), as well as the log pseudo marginal likelihood
(LPML), a leave-one-out fit statistic. Note that
DIC is constructed as DIC3 (see Celeaux et.
al. 2006), where the conditional likehihood evaluated at
the posterior mode is replaced by the marginal predictive
density. Lastly, the random and fixed effects design
matrices, X, Z, are returned that include both the
user input nuisance covariates appended to the time and
treatment-based covariates constructed by
dpgrowmult.n.iter - n.burn)
posterior sampling iterations for every model parameter,
including fixed and random effects.dpgrowmm## extract simulated dataset
library(growcurves)
data(datsimmult)
## Model with DP on clients effects, but now INCLUDE session random effects
## in a multiple membership construction communicated with the N x S matrix, W.subj.aff.
## Returns object, res.mm, of class "dpgrowmm".
shape.dp = 3
res.mult = dpgrowmult(y = datsimmult$y, subject = datsimmult$subject,
trt = datsimmult$trt, time = datsimmult$time,
n.random = datsimmult$n.random, Omega = datsimmult$Omega,
group = datsimmult$group,
subj.aff = datsimmult$subj.aff,
W.subj.aff = datsimmult$W.subj.aff, n.iter = 10000,
n.burn = 2000, n.thin = 10, shape.dp = shape.dp,
option = c("mmi","mmcar"))
plot.results = plot(res.mult) ## ggplot2 plot objects, including growth curves
summary.results = summary(res.mult) ## parameter credible intervals, fit statistics
samples.posterior = samples(res.mult) ## posterior sampled valuesRun the code above in your browser using DataLab