mgcv (version 0.6-1)

predict.gam: Prediction from fitted GAM model

Description

Takes a fitted gam object produced by gam() and produces predictions given a new set of values for the model covariates or the original values used for the model fit.

Usage

predict.gam(object,newdata,type="link",se.fit=FALSE,plot.call=FALSE,...)

Arguments

object

a fitted gam object as produced by gam().

newdata

A data frame containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned.

type

When this has the value "link" (default) the linear predictor (possibly with associated standard errors) is returned. When type="terms" each component of the linear predictor is returned seperately (possibly with standard errors

se.fit

when this is TRUE (not default) standard error estimates are returned for each prediction.

plot.call

used to signal a call from plot.gam, to avoid having to construct a data frame containing all the parametric model components.

...

other arguments.

Value

• If type=="lpmatrix" then a matrix is returned which will give a vector of linear predictor values at the supplied covariate values, when applied to the model coefficient vector. Otherwise, if se.fit is TRUE then a 2 item list is returned with items (both arrays) fit and se.fit containing predictions and associated standard error estimates, otherwise an array of predictions is returned. The dimensions of the returned arrays depends on whether type is "terms" or not: if it is then the array is 2 dimensional with each term in the linear predictor separate, otherwise the array is 1 dimensional and contains the linear predictor/predicted values (or corresponding s.e.s).

WARNING

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

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 plot.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))
newd<-data.frame(x0=(0:30)/30,x1=(0:30)/30,x2=(0:30)/30,x3=(0:30)/30)
pred<-predict.gam(b,newd)