# NOT RUN {
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$x0 <- NULL ## remove x0 from `newd1'
pred1 <- predict(b,newd1,exclude="s(x0)",newdata.guaranteed=TRUE)
#############################################
## difference between "terms" and "iterms"
#############################################
nd2 <- data.frame(x0=c(.25,.5),x1=c(.25,.5),x2=c(.25,.5),x3=c(.25,.5))
predict(b,nd2,type="terms",se=TRUE)
predict(b,nd2,type="iterms",se=TRUE)
#########################################################
## now get variance of sum of predictions using lpmatrix
#########################################################
Xp <- predict(b,newd,type="lpmatrix")
## Xp %*% coef(b) yields vector of predictions
a <- rep(1,31)
Xs <- t(a) %*% Xp ## Xs %*% coef(b) gives sum of predictions
var.sum <- Xs %*% b$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)
##################################################################
# 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)
}
# }
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