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
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/.