ppc.mvgam: Plot conditional posterior predictive checks from 'mvgam'...

In mvgam: Multivariate (Dynamic) Generalized Additive Models

Plot conditional posterior predictive checks from mvgam models

Description

Plot conditional posterior predictive checks from mvgam models

Usage

ppc(object, ...)

## S3 method for class 'mvgam'
ppc(
  object,
  newdata,
  data_test,
  series = 1,
  type = "hist",
  n_bins,
  legend_position,
  xlab,
  ylab,
  ...
)

Arguments

object	list object returned from mvgam. See mvgam()
...	Further par graphical parameters
newdata	Optional dataframe or list of test data containing at least 'series' and 'time' for the forecast horizon, in addition to any other variables included in the linear predictor of formula. If included, the observed values in the test data are compared to the model's forecast distribution for exploring biases in model predictions. Note this is only useful if the same newdata was also included when fitting the original model.
data_test	Deprecated. Still works in place of newdata but users are recommended to use newdata instead for more seamless integration into R workflows
series	integer specifying which series in the set is to be plotted
type	character specifying the type of posterior predictive check to calculate and plot. Valid options are: 'rootogram', 'mean', 'hist', 'density', 'prop_zero', 'pit' and 'cdf'
n_bins	integer specifying the number of bins to use for binning observed values when plotting a rootogram or histogram. Default is 50 bins for a rootogram, which means that if there are >50 unique observed values, bins will be used to prevent overplotting and facilitate interpretation. Default for a histogram is to use the number of bins returned by a call to hist in base R
legend_position	The location may also be specified by setting x to a single keyword from the list "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center". This places the legend on the inside of the plot frame at the given location. Or alternatively, use "none" to hide the legend.
xlab	Label for x axis
ylab	Label for y axis

Details

Conditional posterior predictions are drawn from the fitted mvgam and compared against the empirical distribution of the observed data for a specified series to help evaluate the model's ability to generate unbiased predictions. For all plots apart from type = 'rootogram', posterior predictions can also be compared to out of sample observations as long as these observations were included as 'data_test' in the original model fit and supplied here. Rootograms are currently only plotted using the 'hanging' style.

Note that the predictions used for these plots are conditional on the observed data, i.e. they are those predictions that have been generated directly within the mvgam() model. They can be misleading if the model included flexible dynamic trend components. For a broader range of posterior checks that are created using unconditional "new data" predictions, see pp_check.mvgam

Value

A base R graphics plot showing either a posterior rootogram (for type == 'rootogram'), the predicted vs observed mean for the series (for type == 'mean'), predicted vs observed proportion of zeroes for the series (for type == 'prop_zero'), predicted vs observed histogram for the series (for type == 'hist'), kernel density or empirical CDF estimates for posterior predictions (for type == 'density' or type == 'cdf') or a Probability Integral Transform histogram (for type == 'pit').

Author(s)

Nicholas J Clark

See Also

pp_check.mvgam, predict.mvgam

Examples

## Not run: 
# Simulate some smooth effects and fit a model
set.seed(0)

dat <- mgcv::gamSim(
  1,
  n = 200,
  scale = 2
)

mod <- mvgam(
  y ~ s(x0) + s(x1) + s(x2) + s(x3),
  data = dat,
  family = gaussian(),
  chains = 2,
  silent = 2
)

# Posterior checks
ppc(mod, type = "hist")
ppc(mod, type = "density")
ppc(mod, type = "cdf")

# Many more options are available with pp_check()
pp_check(mod)
pp_check(mod, type = "ecdf_overlay")
pp_check(mod, type = "freqpoly")

## End(Not run)

mvgam documentation built on Jan. 21, 2026, 9:07 a.m.