Description Usage Arguments Value Note Author(s) References See Also Examples
Employs a Dirichlet Process (DP) prior on the set of by-subject random effect parameters
under repeated waves of measurements to allow the number of random effect parameters specified per subject, 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.
1 2 3 | 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)
|
y |
A univariate continuous response, specified as an N x 1 matrix or vector, where |
subject |
The objects on which repeated measures are conducted that serves as the random effects
grouping factor. Input as an N x 1 matrix or vector of subject-measure cases in either
integer or character formt; e.g. |
trt |
An integer or character vector of length |
time |
A univariate vector of length |
n.random |
The desired number of subject random effect terms, |
n.fix_degree |
The desired polynomial order in time to use for generating time-based fix effects.
The fixed effects matrix will be constructed as,
|
formula |
Nuisance fixed and random effects may be entered in |
random.only |
A Boolean variable indicating whether the input formula contains random (for fixed) effects in the case that only
one set are entered. If excluded and |
data |
a |
Omega |
A list object of length equal to the number of multiple membership (MM) effect terms chosen with the |
group |
A list object of length equal to the number of MM terms chosen with prior formulation options |
subj.aff |
A list object of length equal to the number of total MM terms. List element |
W.subj.aff |
A list object of length equal to the number of MM terms. List element |
n.iter |
Total number of MCMC iterations. |
n.burn |
Number of MCMC iterations to discard. |
n.thin |
Gap between successive sampling iterations to save. |
strength.mm |
Sets both the shape and rate parameter for a |
shape.dp |
Shape parameter under a c ~ G(shape.dp, 1) prior on the concentration parameter of the DP (prior
on the set of random effects parameters, b_1, ..., b_n ~ DP(c,G_0)
where |
rate.dp |
Rate parameter under a c ~ G(shape.dp, rate.dp) prior on the concentration parameter of the DP. |
plot.out |
A boolean variable indicating whether user wants to return plots with output results. Defaults to |
option |
A character vector of length equal to the total number of multiple membership terms that supplies the prior formulation choice
for each term. The elements of |
ulabs |
A vector of the same length as |
S3 dpgrowmult
object, for which many methods are available to return and view results. Generic functions applied
to an object, res
of class dpgrow
, includes:
summary(res) |
returns |
print(summary(res)) |
prints contents of summary to console. |
plot(res) |
returns results plots, including the set of subject random effects values and credible intervals, a sample of by-subject growth curves, mean growth curves split by each treatment and control, as well as selected trace plots for number of clusters and for precision parameters for the likehilood and random effects. Lastly, a trace plot for the deviance statistic is also included. |
samples(res) |
contains ( |
resid(res) |
contains the model residuals. |
The intended focus for this package are data where both number of subjects and number of repeated measures are limited. A DP prior
is placed on the by-subject random effects to borrow strength across subjects for each estimation of each subject's growth curve. The
imposition of the DP prior also allows the resulting posterior distributions over the subject random effects to be non-Gaussian.
The dpgrowmult
function generalizes dpgrowmm
by allowing more than one multiple membership effects term.
Terrance Savitsky tds151@gmail.com Susan Paddock paddock@rand.org
S. M. Paddock and T. D. Savitsky (2012) Bayesian Hierarchical Semiparametric Modeling of Longitudinal Post-treatment Outcomes from Open-enrollment Therapy Groups, invited re-submission to: JRSS Series A (Statistics in Society).
T. D. Savitsky and S. M. Paddock (2012) Visual Sufficient Statistics for Repeated Measures data with growcurves for R, submitted to: Journal of Statistical Software.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
## 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 values
## End(Not run)
|
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