Plotting GAM in R: Setting custom x-axis limits?

Question

Is there a way to set the x-axis limits when plotting the predicted fits for GAM models? More specifically, I'm fitting a smoother for each level of a factor using 'by = ', however, each factor level has a different range of values. Plotting the variable in ggplot results in an x-axis that automatically accommodates the different ranges of 'x'; however, after fitting a GAM (mgcv::gam()) the default behavior of plot.gam() appears to be predicting values across a shared x-axis limit.

The dummy data below has some continuous variable for 'x', but in my real data, 'x' is Time (year), and 'group' is sampling location. Because I did not collect data from each site across the same time range, I feel it is inappropriate to show a model fit in these empty years.

library(tidyverse)
library(mgcv)
library(gratia)


theme_set(theme_classic())

## simulate data with a grouping variable of three levels:

d = data.frame(group = rep(c('A','B','C'), each = 100),
               x = c(seq(0,1,length=100),
                     seq(.2,1,length=100),
                     seq(0,.5,length=100))) %>% 
  mutate(y = sin(2*pi*x) + rnorm(100, sd=0.3),
         group = as.factor(group))



## Look at data
ggplot(d, aes(x = x, y = y, colour = group))+
  facet_wrap(~group)+
  geom_point()+
  geom_smooth()

Here is the raw data with loess smoother in ggplot:

## fit simple GAM with smoother for X

m1 = mgcv::gam(y ~ s(x, by = group), data = d)


## base R plot
par(mfrow = c(2,2), bty = 'l', las = 1, mai = c(.6,.6,.2,.1), mgp = c(2,.5,0))
plot(m1)


## Gavin's neat plotter
gratia::draw(m1)

Here is the predicted GAM fit that spans the same range (0,1) for all three groups: Can I limit the prediction/plot to actual values of 'x'?

Answer 1

If you install the current development version (>= 0.6.0.9111) from GitHub, {gratia} will now do what you want, sort of. I added some functionality to smooth_estimates() that I had planned to add eventually but your post kicked it the top of the ToDo list and motivated me to add it now.

You can use smooth_estimates() to evaluate the smooths at the observed (or any user-supplied) data only and then a bit of ggplot() recreates most of the plot.

remotes::install_github("gavinsimpson/gratia")

library('mgcv')
library('gratia')
library('dplyr')
library('ggplot2')

d <- data.frame(group = rep(c('A','B','C'), each = 100),
               x = c(seq(0,1,length=100),
                     seq(.2,1,length=100),
                     seq(0,.5,length=100))) %>% 
  mutate(y = sin(2*pi*x) + rnorm(100, sd=0.3),
         group = as.factor(group))
         
m <- gam(y ~ group + s(x, by = group), data = d, method = 'REML')

sm <- smooth_estimates(m, data = d) %>%
  add_confint()
  
ggplot(sm, aes(x = x, y = est, colour = group)) +
  geom_ribbon(aes(ymin = lower_ci, ymax = upper_ci, colour = NULL, fill = group),
              alpha = 0.2) +
  geom_line() +
  facet_wrap(~ group)