| 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. 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
R (see example code below).
## S3 method for class 'gam'
predict(object,newdata,type="link",se.fit=FALSE,terms=NULL,
exclude=NULL,block.size=NULL,newdata.guaranteed=FALSE,
na.action=na.pass,unconditional=FALSE,...)
object |
a fitted |
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 |
type |
When this has the value |
se.fit |
when this is TRUE (not default) standard error estimates are returned for each prediction. |
terms |
if |
exclude |
if |
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 |
newdata.guaranteed |
Set to |
na.action |
what to do about |
unconditional |
if |
... |
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.
When predicting from models with 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 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 newdata can be a dataframe). See link{linear.functional.terms} for example code.
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.
See the examples for how to use the lpmatrix for obtaining credible
regions for quantities derived from the model.
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.
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
predict.gam() in Splus.
type=="terms" does not exactly match what predict.lm does for
parametric model components.
Simon N. Wood 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).
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.
Wood S.N. (2006b) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
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
#############################################
## 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))))
##################################################################
# 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)
##################################################################
## 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$Vp%*%t(x0));se ## get standard error
## compare to normal prediction
predict(b,newdata=data.frame(x0=xn[1],x1=xn[2],
x2=xn[3],x3=xn[4]),se=TRUE)
####################################################################
## Differentiating the smooths in a model (with CIs for derivatives)
####################################################################
## simulate data and fit model...
dat <- gamSim(1,n=300,scale=sig)
b<-gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot(b,pages=1)
## now evaluate derivatives of smooths with associated standard
## errors, by finite differencing...
x.mesh <- seq(0,1,length=200) ## where to evaluate derivatives
newd <- data.frame(x0 = x.mesh,x1 = x.mesh, x2=x.mesh,x3=x.mesh)
X0 <- predict(b,newd,type="lpmatrix")
eps <- 1e-7 ## finite difference interval
x.mesh <- x.mesh + eps ## shift the evaluation mesh
newd <- data.frame(x0 = x.mesh,x1 = x.mesh, x2=x.mesh,x3=x.mesh)
X1 <- predict(b,newd,type="lpmatrix")
Xp <- (X1-X0)/eps ## maps coefficients to (fd approx.) derivatives
colnames(Xp) ## can check which cols relate to which smooth
par(mfrow=c(2,2))
for (i in 1:4) { ## plot derivatives and corresponding CIs
Xi <- Xp*0
Xi[,(i-1)*9+1:9+1] <- Xp[,(i-1)*9+1:9+1] ## Xi%*%coef(b) = smooth deriv i
df <- Xi%*%coef(b) ## ith smooth derivative
df.sd <- rowSums(Xi%*%b$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)
}