The R Project SVN R-packages

\name{predict.gam}
\alias{predict.gam}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Prediction from fitted GAM model}

\description{ Takes a fitted \code{gam} object produced by \code{gam()} 
and produces predictions given a new set of values for the model covariates 
or the original values used for the model fit. Predictions can be accompanied
by standard errors, based on the posterior distribution of the model
coefficients. The routine can optionally return the matrix by which the model
coefficients must be pre-multiplied in order to yield the values of the linear predictor at
the supplied covariate values: this is useful for obtaining credible regions
for quantities derived from the model (e.g. derivatives of smooths), and for lookup table prediction outside
\code{R} (see example code below).}

\usage{
\method{predict}{gam}(object,newdata,type="link",se.fit=FALSE,terms=NULL,
        exclude=NULL,block.size=NULL,newdata.guaranteed=FALSE,
        na.action=na.pass,unconditional=FALSE,iterms.type=NULL,...)
}
%- maybe also `usage' for other objects documented here.
\arguments{ 
  
 \item{object}{ a fitted \code{gam} object as produced by \code{gam()}.
                }
 \item{newdata}{ A data frame or list 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 \code{newdata} is provided then
                it should contain all the variables needed for prediction: a
                warning is generated if not. See details for use with \code{link{linear.functional.terms}}. } 
 
\item{type}{ When this has the value \code{"link"} (default) the linear predictor (possibly with
associated standard errors) is returned. When \code{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. \code{type="iterms"} is the same, except that any standard errors 
returned for smooth components will include the uncertainty about the intercept/overall mean.  When 
\code{type="response"} predictions 
on the scale of the response are returned (possibly with approximate
standard errors). When \code{type="lpmatrix"} then a matrix is returned
which yields the values of the linear predictor (minus any offset) when
postmultiplied by the
parameter vector (in this case \code{se.fit} is ignored). The latter
option is most useful for getting variance estimates for quantities derived from
the model: for example integrated quantities, or derivatives of smooths. A
linear predictor matrix can also be used to implement approximate prediction
outside \code{R} (see example code, below). }

\item{se.fit}{ when this is TRUE (not default) standard error estimates are returned for each prediction. If set to a number between 0 and 1 then this is taken as the confidence level for intervals that are returned instead.}

\item{terms}{if \code{type=="terms"} or \code{type="iterms"} then only results for the terms (smooth or parametric) named in this array
will be returned. Otherwise any terms not named in this array will be set to zero. If \code{NULL} then all terms are included.}

\item{exclude}{if \code{type=="terms"} or \code{type="iterms"} then terms (smooth or parametric) named in this array will not be returned. Otherwise any terms named in this array will be set to zero. 
If \code{NULL} then no terms are excluded. Note that this is the term names as it appears in the model summary, see example.
You can avoid providing the covariates for excluded smooth terms by setting \code{newdata.guaranteed=TRUE}, which will avoid all
checks on \code{newdata} (covariates for parametric terms can not be skipped).}

\item{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. If \code{NULL} then block size is 1000 if new data supplied, 
and the number of rows in the model frame otherwise. }

\item{newdata.guaranteed}{Set to \code{TRUE} to turn off all checking of
\code{newdata} except for sanity of factor levels: this can speed things up
for large prediction tasks, but \code{newdata} must be complete, with no
\code{NA} values for predictors required in the model. }

\item{na.action}{what to do about \code{NA} values in \code{newdata}. With the
default \code{na.pass}, any row of \code{newdata} containing \code{NA} values
for required predictors, gives rise to \code{NA} predictions (even if the term concerned has no
\code{NA} predictors). \code{na.exclude} or \code{na.omit} result in the
dropping of \code{newdata} rows, if they contain any \code{NA} values for
required predictors. If \code{newdata} is missing then \code{NA} handling is 
determined from \code{object$na.action}.}

\item{unconditional}{if \code{TRUE} then the smoothing parameter uncertainty corrected covariance
matrix is used, when available, otherwise the covariance matrix conditional on the estimated 
smoothing parameters is used. }

\item{iterms.type}{if \code{type="iterms"} then standard errors can either include the uncertainty in the overall mean (default, withfixed and random effects included) or the uncertainty in the mean of the non-smooth fixed effects only (\code{iterms.type=2}).}

\item{...}{ other arguments.}

}

\value{ If \code{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 \code{se.fit} is \code{TRUE} then a 2 item list is returned with items (both arrays) \code{fit}
and \code{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 
\code{type} is \code{"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. If \code{se.fit} is a number between 0 and 1 then in place of \code{se.fit} two arrays are returned \code{ll} and \code{ul} giving the confidence interval limits.   

\code{newdata} can be a data frame, list or model.frame: if it's a model frame
then all variables must be supplied.

}

\details{The standard errors produced by \code{predict.gam} are based on the
Bayesian posterior covariance matrix of the parameters \code{Vp} in the fitted
gam object.

When predicting from models with \code{\link{linear.functional.terms}} then there are two possibilities. If the summation convention is to be used in prediction, as it was in fitting, then \code{newdata} should be a list, with named matrix arguments corresponding to any variables that were matrices in fitting. Alternatively one might choose to simply evaluate the constitutent smooths at particular values in which case arguments that were matrices can be replaced by vectors (and \code{newdata} can be a dataframe). See \code{\link{linear.functional.terms}} for example code.

To facilitate plotting with \code{\link{termplot}}, if \code{object} possesses
an attribute \code{"para.only"} and \code{type=="terms"} then only parametric
terms of order 1 are returned (i.e. those that \code{termplot} can handle).

Note that, in common with other prediction functions, any offset supplied to
\code{\link{gam}} as an argument is always ignored when predicting, unlike
offsets specified in the gam model formula.

See the examples for how to use the \code{lpmatrix} for obtaining credible
regions for quantities derived from the model. 
}

\references{

Chambers and Hastie (1993) Statistical Models in S. Chapman & Hall.

Marra, G and S.N. Wood (2012) Coverage Properties of Confidence Intervals for Generalized Additive
Model Components. Scandinavian Journal of Statistics, 39(1), 53-74. \doi{10.1111/j.1467-9469.2011.00760.x}

Wood S.N. (2017, 2nd ed) Generalized Additive Models: An Introduction with R. Chapman
and Hall/CRC Press.  \doi{10.1201/9781315370279}
}
\author{ Simon N. Wood \email{simon.wood@r-project.org} 

The design is inspired by the S function of the same name described in
Chambers and Hastie (1993) (but is not a clone).

}

\section{WARNING }{
Predictions are likely to be incorrect if data dependent transformations of the covariates
are used within calls to smooths. See examples.

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

\code{type=="terms"} does not exactly match what \code{predict.lm} does for
parametric model components.
} 

\seealso{  \code{\link{gam}}, \code{\link{gamm}}, \code{\link{plot.gam}}}

\examples{
library(mgcv)
n <- 200
sig <- 2
dat <- gamSim(1,n=n,scale=sig)

b <- gam(y~s(x0)+s(I(x1^2))+s(x2)+offset(x3),data=dat)

newd <- data.frame(x0=(0:30)/30,x1=(0:30)/30,x2=(0:30)/30,x3=(0:30)/30)
pred <- predict.gam(b,newd)
pred0 <- predict(b,newd,exclude="s(x0)") ## prediction excluding a term
## ...and the same, but without needing to provide x0 prediction data...
newd1 <- newd;newd1$x0 <- NULL ## remove x0 from `newd1'
pred1 <- predict(b,newd1,exclude="s(x0)",newdata.guaranteed=TRUE)

## custom perspective plot...

m1 <- 20;m2 <- 30; n <- m1*m2
x1 <- seq(.2,.8,length=m1);x2 <- seq(.2,.8,length=m2) ## marginal grid points
df <- data.frame(x0=rep(.5,n),x1=rep(x1,m2),x2=rep(x2,each=m1),x3=rep(0,n))
pf <- predict(b,newdata=df,type="terms")
persp(x1,x2,matrix(pf[,2]+pf[,3],m1,m2),theta=-130,col="blue",zlab="")

#############################################
## difference between "terms" and "iterms"
#############################################
nd2 <- data.frame(x0=c(.25,.5),x1=c(.25,.5),x2=c(.25,.5),x3=c(.25,.5))
predict(b,nd2,type="terms",se=TRUE)
predict(b,nd2,type="iterms",se=TRUE)

#########################################################
## now get variance of sum of predictions using lpmatrix
#########################################################

Xp <- predict(b,newd,type="lpmatrix") 

## Xp \%*\% coef(b) yields vector of predictions

a <- rep(1,31)
Xs <- t(a) \%*\% Xp ## Xs \%*\% coef(b) gives sum of predictions
var.sum <- Xs \%*\% b$Vp \%*\% t(Xs)


#############################################################
## Now get the variance of non-linear function of predictions
## by simulation from posterior distribution of the params
#############################################################

rmvn <- function(n,mu,sig) { ## MVN random deviates
  L <- mroot(sig);m <- ncol(L);
  t(mu + L\%*\%matrix(rnorm(m*n),m,n)) 
}

br <- rmvn(1000,coef(b),b$Vp) ## 1000 replicate param. vectors
res <- rep(0,1000)
for (i in 1:1000)
{ pr <- Xp \%*\% br[i,] ## replicate predictions
  res[i] <- sum(log(abs(pr))) ## example non-linear function
}
mean(res);var(res)

## loop is replace-able by following .... 

res <- colSums(log(abs(Xp \%*\% t(br))))


##################################################################
## The following shows how to use use an "lpmatrix" as a lookup 
## table for approximate prediction. The idea is to create 
## approximate prediction matrix rows by appropriate linear 
## interpolation of an existing prediction matrix. The additivity 
## of a GAM makes this possible. 
## There is no reason to ever do this in R, but the following 
## code provides a useful template for predicting from a fitted 
## gam *outside* R: all that is needed is the coefficient vector 
## and the prediction matrix. Use larger `Xp'/ smaller `dx' and/or 
## higher order interpolation for higher accuracy.  
###################################################################

xn <- c(.341,.122,.476,.981) ## want prediction at these values
x0 <- 1         ## intercept column
dx <- 1/30      ## covariate spacing in `newd'
for (j in 0:2) { ## loop through smooth terms
  cols <- 1+j*9 +1:9      ## relevant cols of Xp
  i <- floor(xn[j+1]*30)  ## find relevant rows of Xp
  w1 <- (xn[j+1]-i*dx)/dx ## interpolation weights
  ## find approx. predict matrix row portion, by interpolation
  x0 <- c(x0,Xp[i+2,cols]*w1 + Xp[i+1,cols]*(1-w1))
}
dim(x0)<-c(1,28) 
fv <- x0\%*\%coef(b) + xn[4];fv    ## evaluate and add offset
se <- sqrt(x0\%*\%b$Vp\%*\%t(x0));se ## get standard error
## compare to normal prediction
predict(b,newdata=data.frame(x0=xn[1],x1=xn[2],
        x2=xn[3],x3=xn[4]),se=TRUE)

##############################################################
## Example of producing a prediction interval for non Gaussian
## data...
##############################################################

f <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 * 
            (10 * x)^3 * (1 - x)^10
set.seed(6);n <- 100;x <- sort(runif(n))
Ey <- exp(f(x)/4);scale <- .5
y <- rgamma(n,shape=1/scale,scale=Ey*scale) ## sim gamma dataexit
b <- gam(y~s(x,k=20),family=Gamma(link=log),method="REML")
Xp <- predict(b,type="lpmatrix")
br <- rmvn(10000,coef(b),vcov(b)) ## 1000 replicate param. vectors
fr <- Xp %*% t(br) ## replicate mean curve estimates
yr <- apply(fr,2,function(x) rgamma(length(x),shape=1/b$scale,
            scale=exp(x)*b$scale)) ## replicate data 
pi <- apply(yr,1,quantile,probs=c(.1,.9),type=9) ## 80% PI
plot(x,y);lines(x,fitted(b));lines(x,pi[1,]);lines(x,pi[2,])
mean(y>pi[1,]&y<pi[2,]) ## check it


##################################################################
# illustration of unsafe scale dependent transforms in smooths....
##################################################################

b0 <- gam(y~s(x0)+s(x1)+s(x2)+x3,data=dat) ## safe
b1 <- gam(y~s(x0)+s(I(x1/2))+s(x2)+scale(x3),data=dat) ## safe
b2 <- gam(y~s(x0)+s(scale(x1))+s(x2)+scale(x3),data=dat) ## unsafe
pd <- dat; pd$x1 <- pd$x1/2; pd$x3 <- pd$x3/2
par(mfrow=c(1,2))
plot(predict(b0,pd),predict(b1,pd),main="b0 and b1 predictions match")
abline(0,1,col=2)
plot(predict(b0,pd),predict(b2,pd),main="b2 unsafe, doesn't match")
abline(0,1,col=2)


####################################################################
## Differentiating the smooths in a model (with CIs for derivatives)
####################################################################

## simulate data and fit model...
dat <- gamSim(1,n=300,scale=sig)
b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot(b,pages=1)

## now evaluate derivatives of smooths with associated standard 
## errors, by finite differencing...
x.mesh <- seq(0,1,length=200) ## where to evaluate derivatives
newd <- data.frame(x0 = x.mesh,x1 = x.mesh, x2=x.mesh,x3=x.mesh)
X0 <- predict(b,newd,type="lpmatrix") 

eps <- 1e-7 ## finite difference interval
x.mesh <- x.mesh + eps ## shift the evaluation mesh
newd <- data.frame(x0 = x.mesh,x1 = x.mesh, x2=x.mesh,x3=x.mesh)
X1 <- predict(b,newd,type="lpmatrix")

Xp <- (X1-X0)/eps ## maps coefficients to (fd approx.) derivatives
colnames(Xp)      ## can check which cols relate to which smooth

par(mfrow=c(2,2))
for (i in 1:4) {  ## plot derivatives and corresponding CIs
  Xi <- Xp*0 
  Xi[,(i-1)*9+1:9+1] <- Xp[,(i-1)*9+1:9+1] ## Xi\%*\%coef(b) = smooth deriv i
  df <- Xi\%*\%coef(b)              ## ith smooth derivative 
  df.sd <- rowSums(Xi\%*\%b$Vp*Xi)^.5 ## cheap diag(Xi\%*\%b$Vp\%*\%t(Xi))^.5
  plot(x.mesh,df,type="l",ylim=range(c(df+2*df.sd,df-2*df.sd)))
  lines(x.mesh,df+2*df.sd,lty=2);lines(x.mesh,df-2*df.sd,lty=2)
}



}
\keyword{models} \keyword{smooth} \keyword{regression}%-- one or more ..