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plot.gam

Plot Components of a GAM Object

Description

A plot method for GAM objects, which can be used on GLM and LM objects as well. It focuses on terms (main-effects), and produces a suitable plot for terms of different types

Usage

## S3 method for class 'Gam'
plot(x, residuals, rugplot, se, scale, ask = FALSE,terms,...)
## S3 method for class 'Gam'
preplot(object, newdata, terms,...)

Arguments

`x`	a `Gam` object, or a `preplot.Gam` object. The first thing `plot.Gam()` does is check if `x` has a component called `preplot`; if not, it computes one using `preplot.Gam()`. Either way, it is this `preplot.Gam` object that is required for plotting a `Gam` object.
`object`	same as `x`
`residuals`	if `TRUE`, partial deviance residuals are plotted along with the fitted terms—default is `FALSE`. If `residuals` is a vector with the same length as each fitted term in `x`, then these are taken to be the overall residuals to be used for constructing the partial residuals.
`rugplot`	if `TRUE` (the default), a univariate histogram or `rugplot` is displayed along the base of each plot, showing the occurrence of each ‘x’; ties are broken by jittering.
`se`	if `TRUE`, upper and lower pointwise twice-standard-error curves are included for each plot. The default is `FALSE`.
`scale`	a lower limit for the number of units covered by the limits on the ‘y’ for each plot. The default is `scale=0`, in which case each plot uses the range of the functions being plotted to create their `ylim`. By setting `scale` to be the maximum value of `diff(ylim)` for all the plots, then all subsequent plots will produced in the same vertical units. This is essential for comparing the importance of fitted terms in additive models.
`ask`	if `TRUE`, `plot.Gam()` operates in interactive mode.
`newdata`	if supplied to `preplot.Gam`, the preplot object is based on them rather than the original.
`terms`	subsets of the terms can be selected
`...`	Additonal plotting arguments, not all of which will work (like xlim)

Value

a plot is produced for each of the terms in the object x. The function currently knows how to plot all main-effect functions of one or two predictors. So in particular, interactions are not plotted. An appropriate ‘x-y’ is produced to display each of the terms, adorned with residuals, standard-error curves, and a rugplot, depending on the choice of options. The form of the plot is different, depending on whether the ‘x’-value for each plot is numeric, a factor, or a matrix.

When ask=TRUE, rather than produce each plot sequentially, plot.Gam() displays a menu listing all the terms that can be plotted, as well as switches for all the options.

A preplot.Gam object is a list of precomputed terms. Each such term (also a preplot.Gam object) is a list with components x, y and others—the basic ingredients needed for each term plot. These are in turn handed to the specialized plotting function gplot(), which has methods for different classes of the leading x argument. In particular, a different plot is produced if x is numeric, a category or factor, a matrix, or a list. Experienced users can extend this range by creating more gplot() methods for other classes. Graphical parameters (see par) may also be supplied as arguments to this function. This function is a method for the generic function plot() for class "Gam".

It can be invoked by calling plot(x) for an object x of the appropriate class, or directly by calling plot.Gam(x) regardless of the class of the object.

Author(s)

Written by Trevor Hastie, following closely the design in the "Generalized Additive Models" chapter (Hastie, 1992) in Chambers and Hastie (1992).

References

Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.

Hastie, T. and Tibshirani, R. (1990) Generalized Additive Models. London: Chapman and Hall.

Examples

data(gam.data)
Gam.object <- gam(y ~ s(x,6) + z,data=gam.data)
plot(Gam.object,se=TRUE)
data(gam.newdata)
preplot(Gam.object,newdata=gam.newdata)

gam

Generalized Additive Models

v1.20

GPL-2

Authors

Trevor Hastie

Initial release

2020-6-27