mgcv (version 0.3-1)

plot.gam: Default GAM plotting

Description

Takes a fitted gam object prodced by gam() and plots the component smooth functions that make it up on the scale of the linear predictor.

Usage

plot.gam(object,rug,se,pages,scale,n)

Arguments

object

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.

se

when TRUE (default) upper and lower lines are added to the plot at 2 standard errors above and below the estimate of the smooth being plotted.

pages

(default 1) the number of pages over which to spread the output. Set to 0 to have the routine leave all graphics settings as they are.

scale

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

n

number of points used for each plot - for a nice smooth plot this needs to be several times the estimated degrees of freedom for the smooth. Default value 100.

Value

• The function simply generates plots.

WARNING

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

Details

Produces default plot showing the smooth components of a fitted GAM. 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 degrees of freedom of the smooth. Within the function, the data for the plots is obtained by a call to predict.gam() with a suitably constructed data frame. A rug plot along the bottom of each frame shows the covariate values.

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)