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 nwrite th 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
.