predictive_error.Rd
This is a convenience function for computing \(y  y^{rep}\)
(insample, for observed \(y\)) or \(y  \tilde{y}\)
(outofsample, for new or heldout \(y\)). The method for stapreg objects
calls posterior_predict
internally, whereas the method for
objects with class "ppd"
accepts the matrix returned by
posterior_predict
as input and can be used to avoid multiple calls to
posterior_predict
.
The rstap modelfitting functions return an object of class
'stapreg'
, which is a list containing at a minimum the components listed
below. Each stapreg
object will also have additional classes (e.g. 'glm')
and several additional components depending on the model and estimation
algorithm.
# S3 method for stapreg predictive_error(object, newsubjdata = NULL, newdistdata = NULL, newtimedata = NULL, draws = NULL, re.form = NULL, seed = NULL, offset = NULL, ...)
object  Either a fitted model object returned by one of the
rstap modeling functions (a stapreg
object) or, for the 

newsubjdata, newdistdata, newtimedata, draws, seed, offset, re.form  Optional arguments passed to

...  Currently ignored. 
A draws
by nrow(newsubjdata)
matrix. If newsubjdata
is
not specified then it will be draws
by nobs(object)
.
The Note section in posterior_predict
about
nnewsubjdata
for binomial models also applies for
predictive_error
, with one important difference. For
posterior_predict
if the lefthand side of the model formula is
cbind(successes, failures)
then the particular values of
successes
and failures
in newsubjdata
don't matter, only
that they add to the desired number of trials. This is not the case
for predictive_error
. For predictive_error
the particular
value of successes
matters because it is used as \(y\) when
computing the error.
stapreg
objectscoefficients
Point estimates, as described in print.stapreg
.
ses
Standard errors based on mad
, as described in
print.stapreg
.
residuals
Residuals of type 'response'
.
fitted.values
Fitted mean values. For GLMs the linear predictors are transformed by the inverse link function.
linear.predictors
Linear fit on the link scale. For linear models this is the same as
fitted.values
.
covmat
Variancecovariance matrix for the coefficients based on draws from the posterior distribution, the variational approximation, or the asymptotic sampling distribution, depending on the estimation algorithm.
model,x,y,z
If requested, the the model frame, model matrix and response variable used, respectively. Note that z corresponds to the fixed covariates, z to the spatial aggregated covariates, and y the response.
family
The family
object used.
call
The matched call.
formula
The model formula
.
data,offset,weights
The data
, offset
, and weights
arguments.
prior.info
A list with information about the prior distributions used.
stapfit,stan_summary
The object of stanfitclass
returned by RStan and a
matrix of various summary statistics from the stapfit object.
rstan_version
The version of the rstan package that was used to fit the model.
posterior_predict
to draw
from the posterior predictive distribution without computing predictive
errors.