This is a convenience function for computing y - yrep
(in-sample, for observed y) or y - ytilde
(out-of-sample, for new or held-out y). The method for stapreg objects
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
The rstap model-fitting functions return an object of class
'stapreg', which is a list containing at a minimum the components listed
stapreg object will also have additional classes (e.g. 'glm')
and several additional components depending on the model and estimation
1 2 3 4
Either a fitted model object returned by one of the
rstap modeling functions (a stapreg
object) or, for the
Optional arguments passed to
nrow(newsubjdata) matrix. If
not specified then it will be
Point estimates, as described in
Standard errors based on
mad, as described in
Residuals of type
Fitted mean values. For GLMs the linear predictors are transformed by the inverse link function.
Linear fit on the link scale. For linear models this is the same as
Variance-covariance matrix for the coefficients based on draws from the posterior distribution, the variational approximation, or the asymptotic sampling distribution, depending on the estimation algorithm.
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 object used.
The matched call.
A list with information about the prior distributions used.
The object of
stanfit-class returned by RStan and a
matrix of various summary statistics from the stapfit object.
The version of the rstan package that was used to fit the model.
The Note section in
nnewsubjdata for binomial models also applies for
predictive_error, with one important difference. For
posterior_predict if the left-hand side of the model formula is
cbind(successes, failures) then the particular values of
newsubjdata don't matter, only
that they add to the desired number of trials. This is not the case
predictive_error the particular
successes matters because it is used as y when
computing the error.
posterior_predict to draw
from the posterior predictive distribution without computing predictive
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