| logpoissonRE.predict {glmmAK} | R Documentation |
This function compute predictive (expected) counts
for specified combinations of covariates. It is based on
the MCMC output obtained using logpoissonRE.
logpoissonRE.predict(nobs, x, xb, offset, cluster,
intcpt.random=FALSE, hierar.center=FALSE,
drandom=c("normal", "gspline"),
betaF, betaR, varR, is.varR=TRUE,
prior.gspline,
probs, values=FALSE,
dir=getwd(), wfile, indfile, header=TRUE, logw, is.indfile,
skip=0, nwrite)
nobs |
number of covariate combinations for which we want to perform a prediction |
x |
covariate combinations for which we want to perform a
prediction.
It should have the same structure as in logpoissonRE used to obtain the MCMC output |
xb |
covariate combinations for which we want to perform a
prediction.
It should have the same structure as in logpoissonRE used to obtain the MCMC output |
offset |
optional offset vector. |
cluster |
vector defining pertinence of the single observations to
clusters. It is useful when we want to predict longitudinal profiles.
See also the same argument in logpoissonRE. |
intcpt.random |
see the same argument in logpoissonRE |
hierar.center |
see the same argument in logpoissonRE |
drandom |
see the same argument in logpoissonRE |
betaF |
sampled values of the fixed effects. This should be a (sub)sample from the MCMC output stored in the file ‘betaF.sim’ |
betaR |
sampled values of the mean of random effects. This should be a
(sub)sample from the MCMC output stored in the file ‘betaR.sim’
It is only needed if hierar.center is TRUE.
|
varR |
sampled values of either (co)variance matrices or precision (matrices) for random effects if there are any. This should be a (sub)sample of either the first or second half of the columns stored in the file ‘varR.sim’ |
is.varR |
logical indicating whether varR gives
(co)variance (is.varR TRUE) or precisions (inverse
variances) (is.varR FALSE) |
prior.gspline |
if drandom is gspline this is a list
specifying the G-splines. It should have the same structure as the
same argument in logpoissonRE used to obtain the MCMC
output. However, it is satisfactory if the items
K, delta and sigma are given. |
probs |
probabilities for which the (pointwise) sample quantiles
of the predictive counts should be computed.
If not given only average (and values) of the predictive counts are computed |
values |
if TRUE also values of the predictive counts at each
(MCMC) iteration are returned.
If FALSE only sample mean (and quantiles) of the predictive
probabilities are returned |
dir |
character giving the directory where the file with (sampled)
G-spline (log-)weights is stored.
Needed only if drandom is gspline.
|
wfile |
character giving the name of the file with (sampled)
G-spline (log-)weights.
Needed only if drandom is gspline. In most cases, for
univariate G-spline this argument will be equal to
“logweight.sim”
and for bivariate G-spline equal to “weight.sim”.
|
indfile |
character giving the name of the file where we stored
indeces of these G-spline components for which the weights are
stored in the file given by wfile. The corresponding file
should have the same structure as ‘knotInd.sim’ created by
logpoissonRE.
Needed only if is.indfile is TRUE. In most cases, for
univariate G-spline it does not have to be specified and for
bivariate G-spline it will be equal to “knotInd.sim”.
|
header |
logical indicating whether the files wfile, indfile
contain a header.
Needed only if drandom is gspline.
|
logw |
logical indicating whether the file wfile contains
logarithms of the weights.
Needed only if drandom is gspline. In most cases, for
univariate G-spline it will be TRUE and for
bivariate G-spline it will be FALSE.
|
is.indfile |
logical.
If TRUE then wfile contains
only the non-zero weights and the G-spline is reconstructed using
indfile.
If FALSE then wfile must contain on
each row weights of all components and indfile is ignored.
Needed only if drandom is gspline and random effects
are bivariate.
|
skip |
number of data rows that should be skipped at the beginning of
the files wfile, indfile.
|
nwrite |
frequency with which is the user informed about the
progress of computation (every nwriteth iteration count of
iterations change) |
A list with the following components (description below applies for
the case with prob=0.5)
Mean |
a matrix with 1 column giving in each row posterior predictive mean of the count E(Y) for a given covariate combination. |
50% |
a matrix with 1 column giving in each row
posterior predictive quantile (here 50% quantile) of the count
for a given covariate combination.
There is one component of this type in the resulting list
for each value of probs.
|
values |
a matrix with n columns, where
n denotes the number of covariate combinations for which we
perform the prediction, and number of rows equal to the length of
the MCMC. Each column gives sampled counts a given covariate combination.
It is returned only if values is TRUE.
|
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
Komárek, A. and Lesaffre, E. (2008). Generalized linear mixed model with a penalized Gaussian mixture as a random-effects distribution. Computational Statistics and Data Analysis, 52, 3441–3458.
logpoissonRE, logpoisson, glm.