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

[Michael Dewey]

At 04:46 27/08/2013, Murat Tasan wrote:
>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 think you just subtract it from y. But perhaps I too am missing the 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

Michael Dewey
info at aghmed.fsnet.co.uk
http://www.aghmed.fsnet.co.uk/home.html