Is it possible to plot the smooth components of a gam fit with ggplot2?

Question

I am fitting a model using gam from the mgcv package and store the result in model and so far I have been looking at the smooth components

Answer 1

FYI, visreg can directly output a gg object:

visreg(model, "x1", gg=TRUE)

Answer 2

Updated to allow user to choose which variables are plotted. Changed 'residuals' term to 'res_data' to avoid conflict with residuals function.

ggplot.model <- function(model, type="conditional", res=FALSE, 
                       col.line="#7fc97f", col.point="#beaed4", size.line=1, size.point=1, no_col = NULL,
                       what = "all") {
  require(visreg)
  require(plyr)
  
  plotdata <- visreg(model, type = type, plot = FALSE)
  smooths <- ldply(plotdata, function(part)   
    data.frame(Variable = part$meta$x, 
             x=part$fit[[part$meta$x]], 
             smooth=part$fit$visregFit, 
             lower=part$fit$visregLwr, 
             upper=part$fit$visregUpr))
  res_data <- ldply(plotdata, function(part)
    data.frame(Variable = part$meta$x, 
               x=part$res[[part$meta$x]], 
               y=part$res$visregRes))
  
   if (what != "all") {
    smooths <- smooths %>%
      filter(lapply(Variable,as.character)%in% what)
    res_data <- res_data%>%
      filter(lapply(Variable,as.character)%in% what)
   }
  
  
  if (res)
    ggplot(smooths, aes(x, smooth)) + geom_line(col=col.line, size=size.line) +
      geom_line(aes(y=lower), linetype="dashed", col=col.line, size=size.line) +
      geom_line(aes(y=upper), linetype="dashed", col=col.line, size=size.line) +
      geom_point(data = res_data, aes(x, y), col=col.point, size=size.point) +
      facet_wrap(. ~ Variable, scales = "free_x", ncol = no_col) + theme_bw()
  else
    ggplot(smooths, aes(x, smooth)) + geom_line(col=col.line, size=size.line) +
      geom_line(aes(y=lower), linetype="dashed", col=col.line, size=size.line) +
      geom_line(aes(y=upper), linetype="dashed", col=col.line, size=size.line) +
      facet_wrap(. ~ Variable, scales = "free_x", ncol=no_col)
}

Answer 3

You can use the visreg package combined with the plyr package. visreg basically plots any model that you can use predict() on.

library(mgcv)
library(visreg)
library(plyr)
library(ggplot2)

# Estimating gam model:
x1 = rnorm(1000)
x2 = rnorm(1000)
n = rpois(1000, exp(x1) + x2^2)
model = gam(n ~ s(x1, k=10) + s(x2, k=20), family="poisson")

# use plot = FALSE to get plot data from visreg without plotting
plotdata <- visreg(model, type = "contrast", plot = FALSE)

# The output from visreg is a list of the same length as the number of 'x' variables,
#   so we use ldply to pick the objects we want from the each list part and make a dataframe: 
smooths <- ldply(plotdata, function(part)   
  data.frame(Variable = part$meta$x, 
             x=part$fit[[part$meta$x]], 
             smooth=part$fit$visregFit, 
             lower=part$fit$visregLwr, 
             upper=part$fit$visregUpr))

# The ggplot:
ggplot(smooths, aes(x, smooth)) + geom_line() +
  geom_line(aes(y=lower), linetype="dashed") + 
  geom_line(aes(y=upper), linetype="dashed") + 
  facet_grid(. ~ Variable, scales = "free_x")

We can put the whole thing into a function, and add an option to show the residuals from the model (res = TRUE):

ggplot.model <- function(model, type="conditional", res=FALSE, 
                       col.line="#7fc97f", col.point="#beaed4", size.line=1, size.point=1) {
  require(visreg)
  require(plyr)
  plotdata <- visreg(model, type = type, plot = FALSE)
  smooths <- ldply(plotdata, function(part)   
    data.frame(Variable = part$meta$x, 
             x=part$fit[[part$meta$x]], 
             smooth=part$fit$visregFit, 
             lower=part$fit$visregLwr, 
             upper=part$fit$visregUpr))
  residuals <- ldply(plotdata, function(part)
    data.frame(Variable = part$meta$x, 
               x=part$res[[part$meta$x]], 
               y=part$res$visregRes))
  if (res)
    ggplot(smooths, aes(x, smooth)) + geom_line(col=col.line, size=size.line) +
      geom_line(aes(y=lower), linetype="dashed", col=col.line, size=size.line) +
      geom_line(aes(y=upper), linetype="dashed", col=col.line, size=size.line) +
      geom_point(data = residuals, aes(x, y), col=col.point, size=size.point) +
      facet_grid(. ~ Variable, scales = "free_x")
  else
    ggplot(smooths, aes(x, smooth)) + geom_line(col=col.line, size=size.line) +
      geom_line(aes(y=lower), linetype="dashed", col=col.line, size=size.line) +
      geom_line(aes(y=upper), linetype="dashed", col=col.line, size=size.line) +
      facet_grid(. ~ Variable, scales = "free_x")
  }

ggplot.model(model)
ggplot.model(model, res=TRUE)

Colors are picked from http://colorbrewer2.org/.