Partial APC plots based on an estimated GAM model — plot

Create the partial APC plots based on an APC model estimated as a semiparametric additive regression model with gam or bam.

Usage

plot_partialAPCeffects(
  model,
  dat,
  variable = "age",
  hide_partialEffects = FALSE,
  vlines_vec = NULL,
  plot_CI = FALSE,
  return_plotData = FALSE
)

Arguments

model: Optional regression model estimated with gam or bam to estimate a smoothed APC surface. Only used if y_var is not specified.
dat: Dataset with columns period and age. If y_var is specified, the dataset must contain the respective column. If model is specified, the dataset must have been used for model estimation with gam or bam.
variable: One of c("age","period","cohort"), specifying the temporal dimension for which the partial effect plots should be created.
hide_partialEffects: If TRUE, only the marginal effect will be plotted. Defaults to FALSE.
vlines_vec: Optional numeric vector of values on the x-axis where vertical lines should be drawn. Can be used to highlight the borders of specific age groups, time intervals or cohorts.
plot_CI: Indicator if 95% confidence intervals for marginal APC effects should be plotted. Only used if hide_partialEffects is set to TRUE. Defaults to FALSE.
return_plotData: If TRUE, a list of the datasets prepared for plotting is returned instead of the ggplot object. The list contains one dataset each for the overall effect (= evaluations of the APC surface to plot the partial effects) and for each marginal APC effect (no matter the specified value of the argument variable). Defaults to FALSE.

Value

ggplot object (if hide_partialEffects is TRUE) or a plot grid created with ggarrange (if FALSE).

Details

If the model was estimated with a log or logit link, the function automatically performs an exponential transformation of the effect.

References

Weigert, M., Bauer, A., Gernert, J., Karl, M., Nalmpatian, A., Küchenhoff, H., and Schmude, J. (2021). Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances. Tourism Economics. doi:10.1177/1354816620987198.

Author

Alexander Bauer alexander.bauer@stat.uni-muenchen.de, Maximilian Weigert maximilian.weigert@stat.uni-muenchen.de

Examples

library(APCtools)
library(mgcv)

data(travel)
model <- gam(mainTrip_distance ~ te(age, period), data = travel)

plot_partialAPCeffects(model, dat = travel, variable = "age")


# mark specific cohorts
plot_partialAPCeffects(model, dat = travel, variable = "cohort",
                       vlines_vec = c(1966.5,1982.5,1994.5))