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 using plot(model)
. I have recently started using ggplot2 and like its output. So I am wondering, is it possible to plot these graphs using ggplot2?
Here is an example:
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")
plot(model, rug=FALSE, select=1)
plot(model, rug=FALSE, select=2)
And I am interest in s(x1, k=10)
and s(x2, k=20)
not in the fit.
Partial answer:
I dug deeper into plot.gam
and mgcv:::plot.mgcv.smooth
and built my own function which extracts the predicted effects and standard errors from the smooth components. It doesn't handle all options and cases of plot.gam
so I only consider it a partial solution, but it works well for me.
EvaluateSmooths = function(model, select=NULL, x=NULL, n=100) {
if (is.null(select)) {
select = 1:length(model$smooth)
}
do.call(rbind, lapply(select, function(i) {
smooth = model$smooth[[i]]
data = model$model
if (is.null(x)) {
min = min(data[smooth$term])
max = max(data[smooth$term])
x = seq(min, max, length=n)
}
if (smooth$by == "NA") {
by.level = "NA"
} else {
by.level = smooth$by.level
}
range = data.frame(x=x, by=by.level)
names(range) = c(smooth$term, smooth$by)
mat = PredictMat(smooth, range)
par = smooth$first.para:smooth$last.para
y = mat %*% model$coefficients[par]
se = sqrt(rowSums(
(mat %*% model$Vp[par, par, drop = FALSE]) * mat
))
return(data.frame(
label=smooth$label
, x.var=smooth$term
, x.val=x
, by.var=smooth$by
, by.val=by.level
, value = y
, se = se
))
}))
}
This returns a "molten" data frame with the smooth components, so it is now possible to use ggplot
with the example above :
smooths = EvaluateSmooths(model)
ggplot(smooths, aes(x.val, value)) +
geom_line() +
geom_line(aes(y=value + 2*se), linetype="dashed") +
geom_line(aes(y=value - 2*se), linetype="dashed") +
facet_grid(. ~ x.var)
If anyone knows a package which allows this in the general case I would be very grateful.
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/.