promodel.RdThe function promodel is a wrapper for dispatching the base
predict and residuals methods to
the procast and proresiduals
functions for probabilistic forecasts and probabilistic residuals, respectively.
a fitted model object for which procast
and/or proresiduals work.
further arguments passed on to procast
or proresiduals, respectively.
The default methods for procast and
proresiduals in this package make a wide range of
different probabilistic forecasts and probabilistic residuals
available for many fitted model object classes. However, it may sometimes be
useful to call these flexible methods via the base
predict and residuals methods.
For example, this may be useful in combination with other packages that
rely on the base functions such as marginaleffects.
Therefore, the promodel wrapper function simply adds an additional
class "promodel" (probabilistic model) to the original class of an
object. Then the methods for predict and residuals then
strip off this class again before calling procast and proresiduals,
respectively.
## Poisson regression model for FIFA 2018 data:
## number of goals scored by each team in each game, explained by
## predicted ability difference of the competing teams
data("FIFA2018", package = "distributions3")
m <- glm(goals ~ difference, data = FIFA2018, family = poisson)
## prediction using a new data set (final of the tournament)
final <- tail(FIFA2018, 2)
## base predict method computes linear predictor on link scale (here in logs)
predict(m, newdata = final)
#> 127 128
#> 0.47275699 -0.04731455
## procast-based method computes distribution object by default
pm <- promodel(m)
predict(pm, newdata = final)
#> distribution
#> 127 Poisson distribution (lambda = 1.6044114)
#> 128 Poisson distribution (lambda = 0.9537873)
## all other procast types are available as well
predict(pm, newdata = final, type = "density", at = 0:4)
#> d_0 d_1 d_2 d_3 d_4
#> 127 0.2010078 0.3224993 0.2587107 0.13835949 0.05549639
#> 128 0.3852791 0.3674743 0.1752462 0.05571586 0.01328527
predict(pm, newdata = final, type = "cdf", at = 0:4)
#> p_0 p_1 p_2 p_3 p_4
#> 127 0.2010078 0.5235071 0.7822178 0.9205773 0.9760737
#> 128 0.3852791 0.7527534 0.9279995 0.9837154 0.9970007
## the base residuals method defaults to deviance residuals
## but the proresiduals-based method defaults to quantile residuals
head(residuals(m))
#> 1 2 3 4 5 6
#> 1.98287409 -1.31569179 -1.43503752 -0.42419390 1.13746339 -0.06469232
head(residuals(pm))
#> 1 2 3 4 5 6
#> 2.2372920 -0.8996531 -0.9809649 -0.4854060 1.5250880 0.5394800