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Display Conditional Effects of Predictors

Source: R/conditional_effects.R
conditional_effects.mvgam.Rd

Display conditional effects of one or more numeric and/or categorical predictors in mvgam models, including two-way interaction effects.

Usage

# S3 method for mvgam
conditional_effects(
  x,
  effects = NULL,
  type = "response",
  points = FALSE,
  rug = FALSE,
  ...
)

# S3 method for mvgam_conditional_effects
plot(x, plot = TRUE, ask = FALSE, ...)

# S3 method for mvgam_conditional_effects
print(x, ...)

Arguments

x

Object of class mvgam or mvgam_conditional_effects

effects

An optional character vector naming effects (main effects or interactions) for which to compute conditional plots. Interactions are specified by a : between variable names. If NULL (the default), plots are generated for all main effects and two-way interactions estimated in the model. When specifying effects manually, all two-way interactions (including grouping variables) may be plotted even if not originally modeled.

type

character specifying the scale of predictions. When this has the value link (default) the linear predictor is calculated on the link scale. If expected is used, predictions reflect the expectation of the response (the mean) but ignore uncertainty in the observation process. When response is used, the predictions take uncertainty in the observation process into account to return predictions on the outcome scale. Two special cases are also allowed: type latent_N will return the estimated latent abundances from an N-mixture distribution, while type detection will return the estimated detection probability from an N-mixture distribution

points

Logical. Indicates if the original data points should be added, but only if type == 'response'. Default is TRUE.

rug

Logical. Indicates if displays tick marks should be plotted on the axes to mark the distribution of raw data, but only if type == 'response'. Default is TRUE.

...

other arguments to pass to plot_predictions

plot

Logical; indicates if plots should be plotted directly in the active graphic device. Defaults to TRUE.

ask

Logical. Indicates if the user is prompted before a new page is plotted. Only used if plot is TRUE. Default is FALSE.

Value

conditional_effects returns an object of class mvgam_conditional_effects which is a named list with one slot per effect containing a ggplot object, which can be further customized using the ggplot2 package. The corresponding plot method will draw these plots in the active graphic device

Details

This function acts as a wrapper to the more flexible plot_predictions. When creating conditional_effects for a particular predictor (or interaction of two predictors), one has to choose the values of all other predictors to condition on. By default, the mean is used for continuous variables and the reference category is used for factors. Use plot_predictions to change these and create more bespoke conditional effects plots.

See also

plot_predictions, plot_slopes

Author

Nicholas J Clark

Examples

# \donttest{
# Simulate some data
simdat <- sim_mvgam(family = poisson(),
                    seasonality = 'hierarchical')

# Fit a model
mod <- mvgam(y ~ s(season, by = series, k = 5) + year:series,
             family = poisson(),
             data = simdat$data_train,
             chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
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#> Chain 2 finished in 2.9 seconds.
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#> Chain 1 finished in 3.2 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 3.0 seconds.
#> Total execution time: 3.4 seconds.
#> 

# Plot all main effects on the response scale
conditional_effects(mod)



# Change the prediction interval to 70% using plot_predictions() argument
# 'conf_level'
conditional_effects(mod, conf_level = 0.7)



# Plot all main effects on the link scale
conditional_effects(mod, type = 'link')



# Works the same for smooth terms, including smooth interactions
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
#> Gu & Wahba 4 term additive model
mod <- mvgam(y ~ te(x0, x1, k = 5) + s(x2, k = 6) + s(x3, k = 6),
            data = dat,
            family = gaussian(),
            chains = 2)
#> Compiling Stan program using cmdstanr
#> 
#> Start sampling
#> Running MCMC with 2 parallel chains...
#> 
#> Chain 1 Iteration:   1 / 1000 [  0%]  (Warmup) 
#> Chain 2 Iteration:   1 / 1000 [  0%]  (Warmup) 
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#> Chain 2 finished in 4.5 seconds.
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#> Chain 1 finished in 4.8 seconds.
#> 
#> Both chains finished successfully.
#> Mean chain execution time: 4.7 seconds.
#> Total execution time: 5.1 seconds.
#> 
conditional_effects(mod)



conditional_effects(mod, conf_level = 0.5, type = 'link')




if (FALSE) {
# ggplot objects can be modified and combined with the help of many
# additional packages. Here is an example using the patchwork package

# Simulate some nonlinear data
dat <- mgcv::gamSim(1, n = 200, scale = 2)
mod <- mvgam(y ~ s(x1, bs = 'moi') +
               te(x0, x2),
             data = dat,
             family = gaussian())

# Extract the list of ggplot conditional_effect plots
m <- plot(conditional_effects(mod), plot = FALSE)

# Add custom labels and arrange plots together using patchwork::wrap_plots()
library(patchwork)
library(ggplot2)
wrap_plots(m[[1]] + labs(title = 's(x1, bs = "moi")'),
           m[[2]] + labs(title = 'te(x0, x2)'))
}
# }