mgcv (version 0.6-1)

plot.gam: Default GAM plotting

Description

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.

Usage

plot.gam(x,rug=TRUE,se=TRUE,pages=0,scale=-1,n=100,n2=40,pers=FALSE,theta=30,phi=30,...)

Arguments

x

a fitted gam object as produced by gam().

rug

when TRUE (default) then the covariate to which the plot applies is displayed as a rug plot at the foot of each plot of a 1-d smooth, and the loactions of the covariates are plotted as points on the contour plot representing a 2-d smooth.

se

when TRUE (default) upper and lower lines are added to the 1-d plots at 2 standard errors above and below the estimate of the smooth being plotted. For 2-d plots, surfaces at +1 and -1 standard errors are contoured and overlayed on the contour

pages

(default 0) the number of pages over which to spread the output. For example, if pages=1 then all terms will be plotted on one page with the layout performed automatically. Set to 0 to have the routine leave all graphics settings as they ar

scale

set to -1 (default) to have the same y-axis scale for each plot, and to 0 for a different y axis for each plot.

n

number of points used for each 1-d 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 2-d functions for contouring.

pers

Set to TRUE if you want perspective plots for 2-d terms.

theta

One of the perspective plot angles.

phi

The other perspective plot angle.

...

other arguments.

Value

• The function simply generates plots.

WARNING

Note that the behaviour of this function is not identical to plot.gam() in Splus.

The function can not deal with smooths of more than 2 variables!

Details

Produces default plot showing the smooth components of a fitted GAM.

For plots of 1-d 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.

Contour plots are produced for 2-d smooths with the x-axes 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. 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. Within the function, the data for the plots is obtained by a call to predict.gam() with a suitably constructed data frame.

Smooths of more than 2 variables are not currently dealt with, but simply generate a warning.

References

Gu and Wahba (1991) Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J. Sci. Statist. Comput. 12:383-398

Wood (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. JRSSB 62(2):413-428

http://www.ruwpa.st-and.ac.uk/simon.html

See Also

gam predict.gam

Examples

library(mgcv)
n<-200
sig2<-4
x0 <- runif(n, 0, 1)
x1 <- runif(n, 0, 1)
x2 <- runif(n, 0, 1)
x3 <- runif(n, 0, 1)
pi <- asin(1) * 2
y <- 2 * sin(pi * x0)
y <- y + exp(2 * x1) - 3.75887
y <- y + 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10 - 1.396
e <- rnorm(n, 0, sqrt(abs(sig2)))
y <- y + e
b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3))
plot(b,pages=1)
# example with 2-d plots
b1<-gam(y~s(x0,x1)+s(x2)+s(x3))
op<-par(mfrow=c(2,2))
plot(b1)
par(op)