On Jun 27, 2011, at 10:45 PM, Remko Duursma wrote:
Dear R-helpers,
I am trying to construct a confidence interval on a prediction of a
gam fit. I have the Wood (2006) book, and section 5.2.7 seems relevant
but I am not able to apply that to this, different, problem.
Any help is appreciated!
Basically I have a function Y = f(X) for two different treatments A
and B. I am interested in the treatment ratios : Y(treatment = B) /
Y(treatment = A) as a function of X, including a confidence interval
for this treatment ratio (because we are testing this ratio against
some value, across the range of X).
The X values that Y is measured at differs between the treatments, but
the ranges are similar.
# Reproducible problem:
X1 <- runif(20, 0.5, 4)
X2 <- runif(20, 0.5, 4)
Y1 <- 20*exp(-0.5*X1) + rnorm(20)
Y2 <- 30*exp(-0.5*X2) + rnorm(20)
# Look at data:
plot(X1, Y1, pch=19, col="blue", ylim=c(0,max(Y1,Y2)), xlim=c(0,5))
points(X2, Y2, pch=19, col="red")
# Full dataset
dfr <- data.frame(X=c(X1,X2), Y=c(Y1,Y2), treatment=c(rep("A",
20),rep("B",20)))
# Fit gam
# I use a gamma family here although it is not necessary: in the real
problem it is, though.
gfit <- gam(Y ~ treatment + s(X), data=dfr, family=Gamma(link=log))
# I am interested in the relationship:
# Y(treatment =="B") / Y(treatment=="A") as a function of X,
Can't you use predict.gam?
plot(predict(gfit, newdata=data.frame(X=rep(seq(0.4, 4, by=0.1), 2),
treatment=c(rep("A",37),rep("B",37) ) ) )[1:37] )
lines(predict(gfit3, newdata=data.frame(X=rep(seq(0.4, 4, by=0.1), 2),
treatment=c(rep("A",37),rep("B",37) ) ) )[-(1:37)])
with a confidence interval!
There is an se.fit argument to predict.gam().
--
David Winsemius, MD
West Hartford, CT
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