[R] predict.glm with constant non-zero response intercept

[Murat Tasan]

hi all -- i'm running into a strange problem that i can't seem to
easily get around, but i'm probably just missing something obvious.

i have a model to which some data is fit using glm with no intercept
term (using my data variables "x" and "y" and a specific link function
"mylink"):

########################################
> foo.glm <- glm(y ~ x + 0, family = Gamma(link = mylink))
> foo.glm

Call:  glm(formula = y ~ x + 0, family = Gamma(link = mylink))

Coefficients:
        x
8.643e-05

Degrees of Freedom: 272 Total (i.e. Null);  271 Residual
Null Deviance:      1821
Residual Deviance: 1135         AIC: -969
> predict(foo.glm, newdata = data.frame(x = c(10e3, 20e3)))
        1         2
0.8642988 1.7285976
########################################

so far, everything is great.
now i'd like to force a constraint on my model: an particular (fixed)
non-zero response variable intercept term, let's call it 0.5.
the only method i know of to accomplish this is to use an offset term
(either in the formula, or with the offset parameter to glm), like so:

########################################
> foo.glm <- glm(y ~ x + 0, offset = rep(0.5, length(y)), family = Gamma(link = mylink))
> foo.glm

Call:  glm(formula = y ~ x + 0, family = Gamma(link = mylink), offset = rep(0.5,
    length(y)))

Coefficients:
        x
8.019e-05

Degrees of Freedom: 272 Total (i.e. Null);  271 Residual
Null Deviance:      1639
Residual Deviance: 1131         AIC: -970.5
########################################

now i'd like to try predict(...) again, but clearly predict.glm(...)
wants to add (actually subtract) the full offset vector used in the
glm(...) call explicitly to my new predictions, which forces recycling
and gives me something like this:

########################################
> predict(foo.glm, newdata = data.frame(x = c(10e3, 20e3)))
  [1] 1.301893 2.103787 1.301893 2.103787 1.301893 2.103787 1.301893
2.103787 1.301893
 [10] 2.103787 1.301893 2.103787 1.301893 2.103787 1.301893 2.103787
1.301893 2.103787
...
[262] 2.103787 1.301893 2.103787 1.301893 2.103787 1.301893 2.103787
1.301893 2.103787
[271] 1.301893 2.103787
########################################

furthermore, with some additional glm-'like' packages (e.g. betareg),
the differing lengths of 'newdata' and the original 'offset' vectors
produce full-on errors.

is there any way in formula(...) other than including offset(...) to
force a CONSTANT non-zero intercept?

thanks for any insight!

-m