Plot 2d smooths in 3d

3d plot of 2d smooths for generalized additive models.

plot_gam_3d(
  model,
  main_var,
  second_var,
  conditional_data = NULL,
  n_plot = 100,
  dmb = FALSE,
  ...
)

Arguments

model	The mgcv gam model
main_var	The 'x' axis.
second_var	The 'y' axis'
conditional_data	Values for other covariates. Default is NULL see details.
n_plot	Points to plot. 100 (the default) works well. Embiggen at the cost of your own waiting time.
dmb	Whether to use plotly's display mode bar. Default is FALSE.
...	Arguments for scico

Value

A plotly surface object

Details

This works like plot_gam_2d, the only difference being that a 3d plot is generated instead. It uses scico for the palette. It is expected that the two input variables are continuous

See also

Other model visualization: plot_coefficients.brmsfit(), plot_coefficients.lm(), plot_coefficients.merMod(), plot_coefficients(), plot_gam_2d(), plot_gam_check(), plot_gam()

Examples

library(mgcv); library(visibly)
set.seed(0)

d = gamSim(2, scale=.1)$data
#> Bivariate smoothing example
mod <- gam(y ~ s(x, z), data = d)
plot_gam_3d(mod, main_var = x, second_var = z)
plot_gam_3d(mod, main_var = x, second_var = z, palette='tokyo')