posterior_predictive {bpr} | R Documentation |
This function is a method for class poisreg
. Compute the posterior predictive distribution and summary statistics for
posterior check of the model;
optionally, it also computes
the predictive distribution with new values of the explanatory variables.
posterior_predictive(object, new_X = NULL)
object |
object of class " |
new_X |
(optional) a data frame in which to look for variables with which to predict. |
The call to this function returns an object of S3 class posterior_check
. The object is a list with the following elements:
data
: the component from object
(list with covariates X
and response variable y
).
y_pred
: matrix of dimension [n, iter]
(with n
sample size), each column is a draw from the posterior predictive distribution.
y_MAP_pred
: vector of length n
containing a draw from the posterior distribution obtained using the maximum a posteriori estimates (MAP) of the parameters.
diagnostics
: list containing 2 elements: CPO
, i.e. the Conditional Predictive Ordinate (Gelfand et al. 1992); and LPML
, i.e.
the logarithm of the pseudo-marginal likelihood (Ibrahim et al. 2014).
newdata
: if the matrix new_X
of new values of the covariates is provided, list of three elements:
new_X
: the provided matrix of explanatory variables;
y_newdata
: a matrix of dimension [nrow(new_X), iter]
, each column is a draw from the posterior predictive distribution using new_X
;
y_MAP_newdata
: vector of length nrow(new_X)
containing a draw from the posterior distribution obtained using the MAP estimate of the parameters,
computed on the new data new_X
.
perc_burnin
: the component from object
.
Gelfand, A., Dey, D. and Chang, H. (1992), Model determination using predictive distributions with implementation via sampling-based-methods (with discussion),
in ‘Bayesian Statistics 4’, University Press.
Ibrahim, J. G., Chen, M.H. and Sinha, D. (2014), Bayesian Survival Analysis, American Cancer Society.