Posterior predictive intervals

Posterior predictive intervals of the model.

Usage

# S3 method for class 'blrmfit'
predictive_interval(object, prob = 0.95, newdata, ...)

Arguments

object

fitted model object

prob

central probability mass to report, i.e. the quantiles 0.5-prob/2 and 0.5+prob/2 are displayed. Multiple central widths can be specified.

newdata

optional data frame specifying for what to predict; if missing, then the data of the input model object is used

...

not used in this function

Value

Matrix with as many rows as the input data set and two columns which contain the lower and upper quantile corresponding to the central probability mass prob for the number of responses of the predictive distribution.

Details

Reports for each row of the input data set the predictive interval according to the fitted model.

Examples

## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(
  OncoBayes2.MC.warmup = 10, OncoBayes2.MC.iter = 20, OncoBayes2.MC.chains = 1,
  OncoBayes2.MC.save_warmup = FALSE
)

example_model("single_agent", silent = TRUE)
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

predictive_interval(blrmfit)
#>   2.5% 97.5%
#> 1    0     1
#> 2    0     1
#> 3    0     2
#> 4    0     2
#> 5    0     2

## Recover user set sampling defaults
options(.user_mc_options)