plot.gam  R Documentation 
Takes a fitted gam
object produced by gam()
and plots the
component smooth functions that make it up, on the scale of the linear
predictor. Optionally produces term plots for parametric model components
as well.
## S3 method for class 'gam' plot(x,residuals=FALSE,rug=NULL,se=TRUE,pages=0,select=NULL,scale=1, n=100,n2=40,n3=3,pers=FALSE,theta=30,phi=30,jit=FALSE,xlab=NULL, ylab=NULL,main=NULL,ylim=NULL,xlim=NULL,too.far=0.1, all.terms=FALSE,shade=FALSE,shade.col="gray80",shift=0, trans=I,seWithMean=FALSE,unconditional=FALSE,by.resids=FALSE, scheme=0,...)
x 
a fitted 
residuals 
If 
rug 
When 
se 
when TRUE (default) upper and lower lines are added to the
1d plots at 2 standard errors
above and below the estimate of the smooth being plotted while for
2d plots, surfaces at +1 and 1 standard errors are contoured
and overlayed on the contour plot for the estimate. If a
positive number is supplied then this number is multiplied by
the standard errors when calculating standard error curves or
surfaces. See also 
pages 
(default 0) the number of pages over which to spread the output. For example,
if 
select 
Allows the plot for a single model term to be selected for printing. e.g. if you just want the plot for the second smooth term set 
scale 
set to 1 (default) to have the same yaxis scale for each plot, and to 0 for a
different y axis for each plot. Ignored if 
n 
number of points used for each 1d plot  for a nice smooth plot this needs to be several times the estimated degrees of freedom for the smooth. Default value 100. 
n2 
Square root of number of points used to grid estimates of 2d functions for contouring. 
n3 
Square root of number of panels to use when displaying 3 or 4 dimensional functions. 
pers 
Set to 
theta 
One of the perspective plot angles. 
phi 
The other perspective plot angle. 
jit 
Set to TRUE if you want rug plots for 1d terms to be jittered. 
xlab 
If supplied then this will be used as the x label for all plots. 
ylab 
If supplied then this will be used as the y label for all plots. 
main 
Used as title (or z axis label) for plots if supplied. 
ylim 
If supplied then this pair of numbers are used as the y limits for each plot. 
xlim 
If supplied then this pair of numbers are used as the x limits for each plot. 
too.far 
If greater than 0 then this is used to determine when a location is too
far from data to be plotted when plotting 2D smooths. This is useful since smooths tend to go wild away from data.
The data are scaled into the unit square before deciding what to exclude, and 
all.terms 
if set to 
shade 
Set to 
shade.col 
define the color used for shading confidence bands. 
shift 
constant to add to each smooth (on the scale of the linear
predictor) before plotting. Can be useful for some diagnostics, or with 
trans 
monotonic function to apply to each smooth (after any shift), before
plotting. Monotonicity is not checked, but default plot limits assume it.

seWithMean 
if 
unconditional 
if 
by.resids 
Should partial residuals be plotted for terms with 
scheme 
Integer or integer vector selecting a plotting scheme for each plot. See details. 
... 
other graphics parameters to pass on to plotting commands. See details for smooth plot specific options. 
Produces default plot showing the smooth components of a
fitted GAM, and optionally parametric terms as well, when these can be
handled by termplot
.
For smooth terms plot.gam
actually calls plot method functions depending on the
class of the smooth. Currently random.effects
, Markov random fields (mrf
),
Spherical.Spline
and factor.smooth.interaction
terms have special methods
(documented in their help files), the rest use the defaults described below.
For plots of 1d smooths, the x axis of each plot is labelled
with the covariate name, while the y axis is labelled s(cov,edf)
where cov
is the covariate name, and edf
the estimated (or user defined for regression splines)
degrees of freedom of the smooth. scheme == 0
produces a smooth curve with dashed curves
indicating 2 standard error bounds. scheme == 1
illustrates the error bounds using a shaded
region.
For scheme==0
, contour plots are produced for 2d smooths with the xaxes labelled with the first covariate
name and the y axis with the second covariate name. The main title of
the plot is something like s(var1,var2,edf)
, indicating the
variables of which the term is a function, and the estimated degrees of
freedom for the term. When se=TRUE
, estimator variability is shown by overlaying
contour plots at plus and minus 1 s.e. relative to the main
estimate. If se
is a positive number then contour plots are at plus or minus se
multiplied
by the s.e. Contour levels are chosen to try and ensure reasonable
separation of the contours of the different plots, but this is not
always easy to achieve. Note that these plots can not be modified to the same extent as the other plot.
For 2d smooths scheme==1
produces a perspective plot, while scheme==2
produces a heatmap,
with overlaid contours and scheme==3
a greyscale heatmap (contour.col
controls the
contour colour).
Smooths of 3 and 4 variables are displayed as tiled heatmaps with overlaid contours. In the 3 variable case the third variable is discretized and a contour plot of the first 2 variables is produced for each discrete value. The panels in the lower and upper rows are labelled with the corresponding third variable value. The lowest value is bottom left, and highest at top right. For 4 variables, two of the variables are coarsely discretized and a square array of image plots is produced for each combination of the discrete values. The first two arguments of the smooth are the ones used for the image/contour plots, unless a tensor product term has 2D marginals, in which case the first 2D marginal is image/contour plotted. n3
controls the number of panels.
See also vis.gam
.
Fine control of plots for parametric terms can be obtained by calling
termplot
directly, taking care to use its terms
argument.
Note that, if seWithMean=TRUE
, the confidence bands include the uncertainty about the overall mean. In other words
although each smooth is shown centred, the confidence bands are obtained as if every other term in the model was
constrained to have average 0, (average taken over the covariate values), except for the smooth concerned. This seems to correspond more closely to how most users interpret componentwise intervals in practice, and also results in intervals with
close to nominal (frequentist) coverage probabilities by an extension of Nychka's (1988) results presented in Marra and Wood (2012). There are two possible variants of this approach. In the default variant the extra uncertainty is in the mean of all other terms in the model (fixed and random, including uncentred smooths). Alternatively, if seWithMean=2
then only the uncertainty in parametric fixed effects is included in the extra uncertainty (this latter option actually tends to lead to wider intervals when the model contains random effects).
Several smooth plots methods using image
will accept an hcolors
argument, which can be anything documented in heat.colors
(in which case something like hcolors=rainbow(50)
is appropriate), or the grey
function (in which case somthing like hcolors=grey(0:50/50)
is needed). Another option is contour.col
which will set the contour colour for some plots. These options are useful for producing grey scale pictures instead of colour.
Sometimes you may want a small change to a default plot, and the arguments to plot.gam
just won't let you do it.
In this case, the quickest option is sometimes to clone the smooth.construct
and Predict.matrix
methods for
the smooth concerned, modifying only the returned smoother class (e.g. to foo.smooth
).
Then copy the plot method function for the original class (e.g. mgcv:::plot.mgcv.smooth
), modify the source code to plot exactly as you want and rename the plot method function (e.g. plot.foo.smooth
). You can then use the cloned
smooth in models (e.g. s(x,bs="foo")
), and it will automatically plot using the modified plotting function.
The functions main purpose is its side effect of generating plots. It also silently returns a list of the data used to produce the plots, which can be used to generate customized plots.
Note that the behaviour of this function is not identical to
plot.gam()
in SPLUS.
Plotting can be slow for models fitted to large datasets. Set rug=FALSE
to improve matters.
If it's still too slow set too.far=0
, but then take care not to overinterpret smooths away from
supporting data.
Plots of 2D smooths with standard error contours shown can not easily be customized.
all.terms
uses termplot
which looks for the original data in the environment of the fitted model object formula. Since gam
resets this environment to avoid large saved model objects containing data in hidden environments, this can fail.
Simon N. Wood simon.wood@rproject.org
Henric Nilsson henric.nilsson@statisticon.se donated the code for the shade
option.
The design is inspired by the S function of the same name described in Chambers and Hastie (1993) (but is not a clone).
Chambers and Hastie (1993) Statistical Models in S. Chapman & Hall.
Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics.
Nychka (1988) Bayesian Confidence Intervals for Smoothing Splines. Journal of the American Statistical Association 83:11341143.
Wood S.N. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC Press.
gam
, predict.gam
, vis.gam
library(mgcv) set.seed(0) ## fake some data... f1 < function(x) {exp(2 * x)} f2 < function(x) { 0.2*x^11*(10*(1x))^6+10*(10*x)^3*(1x)^10 } f3 < function(x) {x*0} n<200 sig2<4 x0 < rep(1:4,50) x1 < runif(n, 0, 1) x2 < runif(n, 0, 1) x3 < runif(n, 0, 1) e < rnorm(n, 0, sqrt(sig2)) y < 2*x0 + f1(x1) + f2(x2) + f3(x3) + e x0 < factor(x0) ## fit and plot... b<gam(y~x0+s(x1)+s(x2)+s(x3)) plot(b,pages=1,residuals=TRUE,all.terms=TRUE,shade=TRUE,shade.col=2) plot(b,pages=1,seWithMean=TRUE) ## better coverage intervals ## just parametric term alone... termplot(b,terms="x0",se=TRUE) ## more use of color... op < par(mfrow=c(2,2),bg="blue") x < 0:1000/1000 for (i in 1:3) { plot(b,select=i,rug=FALSE,col="green", col.axis="white",col.lab="white",all.terms=TRUE) for (j in 1:2) axis(j,col="white",labels=FALSE) box(col="white") eval(parse(text=paste("fx < f",i,"(x)",sep=""))) fx < fxmean(fx) lines(x,fx,col=2) ## overlay `truth' in red } par(op) ## example with 2d plots, and use of schemes... b1 < gam(y~x0+s(x1,x2)+s(x3)) op < par(mfrow=c(2,2)) plot(b1,all.terms=TRUE) par(op) op < par(mfrow=c(2,2)) plot(b1,all.terms=TRUE,scheme=1) par(op) op < par(mfrow=c(2,2)) plot(b1,all.terms=TRUE,scheme=c(2,1)) par(op) ## 3 and 4 D smooths can also be plotted dat < gamSim(1,n=400) b1 < gam(y~te(x0,x1,x2,d=c(1,2),k=c(5,15))+s(x3),data=dat) ## Now plot. Use cex.lab and cex.axis to control axis label size, ## n3 to control number of panels, n2 to control panel grid size, ## scheme=1 to get greyscale... plot(b1,pages=1)
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