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