When I try to get a prediction for just one value of the independent variable using this syntax:
library(survey)
library(splines)
data(api)
dclus <- svydesign(id=~dnum,data=apiclus2)
log<-svyglm(api99 ~ bs(ell,degree=1, knots =c(14,23)) , dclus)
data <- data.frame(ell = 0)
data <- cbind(data, predict(log, newdata=data))
data <- data.frame(ell = 15)
data <- cbind(data, predict(log, newdata=data))
I get always the same prediction:
#link=591.0929
This does not happen if I only use survey or spline or if I create a data frame with a list of independent values:
data<-data.frame(ell = rep(seq(from = 0, to = 66)))
data <- cbind(data, predict(log, newdata=data))
Curiously enough, in this last data frame link=591.0929 corresponds to ell=23
The issue here is that the bs() term doesn't fully specify the basis -- it also uses the range of the predictor to work out boundary knots. With only one point, the way it does this doesn't work.
A work-around is to specify the boundary knots, eg,
> log<-svyglm(api99 ~ bs(ell,degree=1, knots =c(14,23), Boundary.knots=c(0,100)) , dclus)
> data <- data.frame(ell = 0)
> predict(log, newdata=data)
link SE
1 787.64 27.162
> data2 <- data.frame(ell = 15)
> predict(log, newdata=data2)
link SE
1 627.76 34.108
It looks as though predict.lm has some complicated stuff to stop this happening that wasn't there when predict.svyglm was written.
I'll pass this on to the package maintainer ;-)