Obtains out-of-sample posterior predictions under the fitted parametric
Bayesian model for ecological inference. predict method for class
eco and ecoX.
# S3 method for ecoX
predict(
object,
newdraw = NULL,
subset = NULL,
newdata = NULL,
cond = FALSE,
verbose = FALSE,
...
)predict.eco yields a matrix of class predict.eco
containing the Monte Carlo sample from the posterior predictive distribution
of inner cells of ecological tables. summary.predict.eco will
summarize the output, and print.summary.predict.eco will print the
summary.
An output object from eco or ecoNP.
An optional list containing two matrices (or three
dimensional arrays for the nonparametric model) of MCMC draws of \(\mu\)
and \(\Sigma\). Those elements should be named as mu and
Sigma, respectively. The default is the original MCMC draws stored in
object.
A scalar or numerical vector specifying the row number(s) of
mu and Sigma in the output object from eco. If
specified, the posterior draws of parameters for those rows are used for
posterior prediction. The default is NULL where all the posterior
draws are used.
An optional data frame containing a new data set for which posterior predictions will be made. The new data set must have the same variable names as those in the original data.
logical. If TRUE, then the conditional prediction will
made for the parametric model with contextual effects. The default is
FALSE.
logical. If TRUE, helpful messages along with a
progress report on the Monte Carlo sampling from the posterior predictive
distributions are printed on the screen. The default is FALSE.
further arguments passed to or from other methods.
The posterior predictive values are computed using the Monte Carlo sample
stored in the eco output (or other sample if newdraw is
specified). Given each Monte Carlo sample of the parameters, we sample the
vector-valued latent variable from the appropriate multivariate Normal
distribution. Then, we apply the inverse logit transformation to obtain the
predictive values of proportions, \(W\). The computation may be slow
(especially for the nonparametric model) if a large Monte Carlo sample of
the model parameters is used. In either case, setting verbose = TRUE
may be helpful in monitoring the progress of the code.
eco, predict.ecoNP