| predict.gam {mgcv} | R Documentation |
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.
predict.gam(object,newdata,type="link",se.fit=FALSE,...)
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. If newdata is provided then
it should contain all the variables needed for prediction: a
warning is generated if not.
|
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): this includes
parametric model components, followed by each smooth component, but excludes any offset. When type="response" predictions
on the scale of the response are returned (possibly with approximate
standard errors). When type="lpmatrix" then a matrix is returned
which yields the values of the linear predictor (minus any offset) when applied to the
parameter vector (in this case se.fit is ignored). The latter
option is most useful for getting variance estimates for integrated quantities. |
se.fit |
when this is TRUE (not default) standard error estimates are returned for each prediction. |
... |
other arguments. |
The standard errors produced by predict.gam are based on the
Bayesian posterior covariance matrix of the parameters Vp in the fitted
gam object.
If type=="lpmatrix" then a matrix is returned which will
give a vector of linear predictor values (minus any offest) 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). The linear predictor returned termwise will
not include the offset.
newdata can be a data frame, list or model.frame: if it's a model frame
then all variables must be supplied.
Note that the behaviour of this function is not identical to
predict.gam() in Splus.
Note that the
Simon N. Wood simon@stats.gla.ac.uk
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
http://www.stats.gla.ac.uk/~simon/
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)