R help - Nov 2012 - Problem with glm, gaussian family with log-link

Dear all,

I am using the book "Generalized Linera Models and Extension" by
Hardin and
Hilbe (second edition, 2007) at the moment. The authors suggest that
instead of OLS models, "the log link is generally used for response data
that take only positive values on the continuous scale". Of course they
also suggest residual plots to check whether a "normal" linera model
using
an identity link can still be used.

I am trying to replicate in R what they do in the book in STATA. Indeed, I
have no problems in STATA with the log link. However, when calling the same
model using R's glm-function, but specifying
*family=gaussian(link="log") *I
am asked to provide starting values. When I set them all equal to zero, I
always get the message that the algorithm did not converge. Picking other
values the message is sometimes the same, but more often I get:
*
*
*Error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  : *
*  NA/NaN/Inf in 'x' *
*
*
As I said, in STATA I can run these models without setting starting values
and without errors. I tried many different models, and different datasets,
but the problem is always the same (unless I only include one single
independent variable). Could anyone tell me why this is the case, or what I
do wrong, or why the suggested models from the book might not be
appropriate? I'd appreciate any help!

Best,
Florian

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On Mon, Nov 26, 2012 at 5:33 AM, Florian Weiler <fweiler08@jhubc.it>
wrote:
> Dear all,
>
> I am using the book "Generalized Linera Models and Extension" by
Hardin and
> Hilbe (second edition, 2007) at the moment. The authors suggest that
> instead of OLS models, "the log link is generally used for response
data
> that take only positive values on the continuous scale".

<snip>
> specifying *family=gaussian(link="log") *I
> am asked to provide starting values. When I set them all equal to zero, I
> always get the message that the algorithm did not converge. Picking other
> values the message is sometimes the same, but more often I get:
> *
> *
> *Error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,  :
> *
> *  NA/NaN/Inf in 'x' *
> *
> *
> As I said, in STATA I can run these models without setting starting values
> and without errors. I tried many different models, and different datasets,
>
And yet you've failed to provide even one of them together with your code
as a reproducible example ...

# This works without starting values:
set.seed(2341)
x <- rep(1:10,3) ; y <- jitter(rpois(30,5+x))
plot(x,y)
(gausslog <- glm(y~x,family=gaussian(link='log')))
exp(coef(gausslog))

# This works only with starting values
set.seed(2341)
x <- rep(1:10,3) ; y <- jitter(rpois(30,x))
plot(x,y) ; summary(y)  # yes,yes, some y <0, just trying to reproduce the
error...
(gausslog <- glm(y~x,family=gaussian(link='log')))
(gausslog <- glm(y~x,family=gaussian(link='log'),start=c(0,0)))

# also
set.seed(2341)
x <- rep(1:10,3) ; y <- rnorm(30,0+0.1*x)
plot(x,y) ; summary(y)
(gausslog <- glm(y~x,family=gaussian(link='log'),start=c(0,0)))

So really this is a non issue without the offending data set and code.

but the problem is always the same (unless I only include one
single
> independent variable).

Oh, more information... way to build up the suspense

set.seed(2341)
x <- rep(1:10,3) ; xx <- rep(seq(20,50,l=5),6) ; y <-
rnorm(30,5+3*x-2*xx)
(gausslog <- glm(y~x+xx,family=gaussian(link='log'),start=c(0,0,0)))

No joy. Still fits.

> Could anyone tell me why this is the case, or what I
> do wrong,

No

> or why the suggested models from the book might not be
> appropriate? I'd appreciate any help!
>
> Personally I don't care for reproducing some results from STATA and
have
no comment on the validity of the above but maybe someone in the list would
have a better answer if you repost.




> Best,
> Florian
>
>
Also this:
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>
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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