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. The resulting joint marginal distribution over the data is a
DP mixture.
ddpgrow(y, subject, trt, time, n.random, n.fix_degree, formula, random.only, data, dosemat, numdose, typetreat, labt, Omega, n.iter, n.burn, n.thin, shape.dp, rate.dp, M.init, plot.out)N
captures the number of subject-time cases (repeated subject measures). Data may reflect unequal
number of measures per subject. Missing occasions are left out as no NA values are allowed.(1,1,1,2,2,3,3,3,...,n,n,n), where n is the total
number of subjects.N (number of cases) indicating treatment
arm assignments for each case. May also be input as length n vector, where n is
the number of unique subjects, indicating subject arm assignment. Multiple treatment arms
are allowed and if the vector is entered as numeric, e.g. (0,1,2,3,..), the lowest numbered
arm is taken as baseline (captured by global fixed effects). If entered in character format,
the first treatment entry is taken as baseline. If the are no treatment (vs. control) arm,
then this vector is composed of a single value for all entries.N, capturing the time points associated to each by-subject
measure.q. May
be set equal to the number of measurement waves, T. The y, trt, time vectors will together
be used to create both fixed and random effect design matrices. The random effects matrix will be of the
the form, (1, time, ... , time^(n.random - 1)) (grouped, by subject).
This formulation is a growth curve model that allows assessment of by-treatment effects and by-client growth curves.(time, ..., time^(n.fix_degree), trt_1,time*trt_1, ... ,time^(n.fix_degree)*trt_l, trt_L,..., time^(n.fix_degree)*trt_L).formula with the following format,
y ~ x_1 + x_2*x_3 | z_1*z_2 . The bar, |, separates fixed and random effects. If it
is only desired to enter either fixed or random effects, but not both then the | may be omitted. Note:
the nuisance random effects are assumed to be grouped by subject. The fixed and random effects valules may change with
each repeated measure; however, within subject growth curves will keep constant z and x values between
measurement waves. It is possible to bypass the growth curve construction by leaving y, trt, time, n.random
blank and entering only formula, instead. The model output plots, will, however
exclude growth curves in that event. If a formula is input and a response, y, is included, then
the parameter input y may be omitted. If the y input is included, it will be over-written by that from formula.formula is entered without a |, random.only defaults to
FALSE.data.frame containing the variables named in formula.n x (sum(numdose)+1) matrix object that maps subjects to treatment dosages. The first column should be an
intercept column (filled with 1's). If there is only a single treatment arm, then the number of columns in dosemat should be sum(numdose).
There is always a leave-one-out dosage for dosemat. For multiple treatment arms, the null (0) treatment is the one left out.typetreat.c("car","mvn","ind").typetreat) providing user names for each treatment. The names are
used in plot objects. If NULL, then the numerical order of treatment entries are used."car" %in% typetreat.
List element m contains an numdose[m] x numdose[m] numeric matrix to encode the CAR adjacency matrix,
where numdose[m] is the number of dosages receiving the multivariate CAR prior.
This input is required only under "car" %in% typetreat.ddpgrow will return (n.iter - n.burn) posterior samples.n is the total number of subjects.TRUE.dpgrow object, for which many methods are available to return and view results. Generic functions applied
to an object, res of class dpgrow, includes:
, includes:T. D. Savitsky and S. M. Paddock (2011) Visual Sufficient Statistics for Repeated Measures data with growcurves for R, submitted to: Journal of Statistical Software.
dpgrowmult, dpgrowmm, dpgrow
## Not run:
# ## extract simulated dataset
# library(growcurves)
# data(datddpsim)
# ## attach(datddpsim)
# ## run dpgrow mixed effects model; returns object of class "ddpgrow"
# shape.dp = 4
# res = ddpgrow(y = dat$y, subject = dat$subject,
# trt = dat$trt, time = dat$time,
# typetreat = c("mvn","car","ind","car"),
# numdose = dat$numdose,
# labt = dat$labt, dosemat = dat$dosemat,
# Omega = dat$Omega, n.random = dat$n.random,
# n.fix_degree = 2, n.iter = 10000, n.burn = 2000,
# n.thin = 10, shape.dp = 1)
# plot.results = plot(res) ## ggplot2 plot objects, including growth curves
# summary.results = summary(res) ## parameter credible intervals, fit statistics
# samples.posterior = samples(res) ## posterior sampled values
# ## End(Not run)
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