plot.gam {mgcv} | 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.

plot.gam(x,residuals=FALSE,rug=TRUE,se=TRUE,pages=0,select=NULL, scale=-1,n=100,n2=40,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",...)

`x` |
a fitted `gam` object as produced by `gam()` . |

`residuals` |
If `TRUE` then partial residuals are added to plots of 1-D smooths. If `FALSE`
then no residuals are added. If this is an array of the correct length then it is used as the array of
residuals to be used for producing partial residuals. If `TRUE` then the
residuals are the working residuals from the IRLS iteration weighted by the
IRLS weights. Partial residuals for a smooth term are the
residuals that would be obtained by dropping the term concerned from the model, while leaving all other
estimates fixed (i.e. the estimates for the term plus the residuals). |

`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 locations 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 while for
2-d 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 `shade` , below. |

`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 are. |

`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 `select=2` . |

`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. Ignored if `ylim` supplied. |

`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. |

`jit` |
Set to TRUE if you want rug plots for 1-d 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 2-D 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 `too.far` is a distance
within the unit square. |

`all.terms` |
if set to `TRUE` then the partial effects of parametric
model components are also plotted, via a call to `termplot` . Only
terms of order 1 can be plotted in this way. |

`shade` |
Set to `TRUE` to produce shaded regions as confidence bands
for smooths (not avaliable for parametric terms, which are plotted using `termplot` ). |

`shade.col` |
define the color used for shading confidence bands. |

`...` |
other graphics parameters to pass on to plotting commands. |

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 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. 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.

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

.

Fine control of plots for parametric terms can be obtained by calling
`termplot`

directly, taking care to use its `terms`

argument.

The function simply generates plots.

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

in S-PLUS.

Plots of 2-D smooths with standard error contours shown can not easily be customized.

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

Simon N. Wood simon.wood@r-project.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.

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, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413-428

Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114

Wood, S.N. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass. 99:637-686

http://www.stats.gla.ac.uk/~simon/

library(mgcv) set.seed(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) y <- 2 * 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 x0 <- factor(x0) b<-gam(y~x0+s(x1)+s(x2)+s(x3)) plot(b,pages=1,residuals=TRUE,all.terms=TRUE,shade=TRUE,shade.col=2) # just parametric term alone termplot(b,terms="x0",se=TRUE) # example with 2-d plots b1<-gam(y~x0+s(x1,x2)+s(x3)) op<-par(mfrow=c(2,2)) plot(b1,all.terms=TRUE) par(op)

[Package *mgcv* version 1.2-3 Index]