#####################################################################
#
# Package informativeDropout implements Bayesian and Frequentist
# approaches for fitting varying coefficient models in longitudinal
# studies with informative dropout
#
# Copyright (C) 2014 University of Colorado Denver.
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
#####################################################################
#####################################
# Demo of a dirichlet process model with a gaussian outcome
#####################################
# load the simulated data
data(sim_1)
data <- sim_1
# define the model options
model.options=dirichlet.model.options(iterations=100, n.clusters=15, burnin=0, print=10,
dropout.estimationTimes = seq(1/15,1,1/15),
dp.concentration=1,
dp.concentration.alpha=1,
dp.concentration.beta=1,
dp.cluster.sigma = diag(3),
dp.cluster.sigma.nu0 = 5,
dp.cluster.sigma.T0 = diag(3),
dp.dist.mu0 = c(0,0,0),
dp.dist.mu0.mb = c(0,0,0),
dp.dist.mu0.Sb = diag(3),
dp.dist.sigma0 = diag(3),
dp.dist.sigma0.nub = 5,
dp.dist.sigma0.Tb = diag(3),
betas.covariates = NULL,
betas.covariates.mu = NULL,
betas.covariates.sigma = NULL,
sigma.error.tau=0.01)
# Set the columns to be used in the model
ids.var = "patid"
outcomes.var = "yi_bin"
groups.var = NULL
covariates.var = NULL
times.dropout.var = "drop"
times.observation.var = "t"
# set the model fitting method
method="dirichlet"
# set the distribution of the outcome
dist = "binary"
# set a random seed
set.seed(1066)
# fit the model
fit = informativeDropout(data, model.options, ids.var,
outcomes.var, groups.var,
covariates.var,
times.dropout.var, times.observation.var,
method=method, dist=dist)
# summarise the result
summary(fit)
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