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,terms=NULL, block.size=1000,newdata.guaranteed=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 and any intercept. 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. 
terms 
if type=="terms" then only results for the terms given in this array
will be returned. 
block.size 
maximum number of predictions to process per call to underlying code: larger is quicker, but more memory intensive. Set to < 1 to use total number of predictions as this. 
newdata.guaranteed 
Set to TRUE to turn off all checking of
newdata except for sanity of factor levels: this can speed things up
for large prediction tasks, but newdata must be complete. 
... 
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.
To facilitate plotting with termplot
, if object
possesses
an attribute "para.only"
and type=="terms"
then only parametric
terms of order 1 are returned (i.e. those that termplot
can handle).
Note that, in common with other prediction functions, any offset supplied to
gam
as an argument is always ignored when predicting, unlike
offsets specified in the gam model formula.
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 or the intercept.
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.
Simon N. Wood simon.wood@rproject.org
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. 12:383398
Wood, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413428
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95114
http://www.stats.gla.ac.uk/~simon/
library(mgcv) n<200 sig < 2 x0 < runif(n, 0, 1) x1 < runif(n, 0, 1) x2 < runif(n, 0, 1) x3 < runif(n, 0, 1) y < 2 * sin(pi * x0) y < y + exp(2 * x1) y < y + 0.2 * x2^11 * (10 * (1  x2))^6 + 10 * (10 * x2)^3 * (1  x2)^10 y < y + x3 e < rnorm(n, 0, sig) y < y + e b<gam(y~s(x0)+s(I(x1^2))+s(x2)+offset(x3)) rm(y,x0,x1,x2,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) ## now get variance of sum of predictions using lpmatrix Xp < predict(b,newd,type="lpmatrix") ## Xp a < rep(1,31) Xs < t(a) var.sum < Xs ## Now get the variance of nonlinear function of predictions ## by simulation from posterior distribution of the params library(MASS) br<mvrnorm(1000,coef(b),b$Vp) ## 1000 replicate param. vectors res < rep(0,1000) for (i in 1:1000) { pr < Xp res[i] < sum(log(abs(pr))) ## example nonlinear function } mean(res);var(res) ## note: loop is replaceable by res < colSums(log(abs(Xp