@Duncan, You make a very good point. Somehow I overlooked that 0 is not
positive. I guess that rules out the log normal model.
My challenge here is finding the right model for this data. Originally it was
a nice count of students. Relatively easy to model with a zero inflated
Poisson model. The resulting residuals seemed reasonable.
However, I was then instructed to change the count of students to a "rate"
which was calculated as students / population (Each school has its own
population.)) This is now no longer a count variable, but a proportion between
0 and 1.
This "rate" (students/population) is no longer Poisson, but is certainly not
normal either. So, I'm a bit lost as to the appropriate distribution to
represent it.
Any thoughts?
--
Noah Silverman, M.S.
UCLA Department of Statistics
8117 Math Sciences Building
Los Angeles, CA 90095
On Apr 16, 2013, at 12:44 PM, Thomas Lumley <tlum...@uw.edu> wrote:
> On Wed, Apr 17, 2013 at 5:19 AM, Noah Silverman <noahsilver...@ucla.edu>
> wrote:
> Hi,
>
> I have some data, that when plotted looks very close to a log-normal
> distribution. My goal is to build a regression model to test how this
> variable responds to several independent variables.
>
> [snip]
>
> When I try to build a simple model, I also get an error:
>
> l <- glm(y~ x, family=gaussian(link="log"))
>
> Error in eval(expr, envir, enclos) : cannot find valid starting values:
> please specify some
>
>
> Duncan has described the problems with the lognormal. I will just point out
> that this 'simple model' is not lognormal. It is a model with normal errors
> and log link, ie.
>
> y ~ N(mu, sigma^2)
> log(mu) = x \beta
>
>
> -thomas
>
> --
> Thomas Lumley
> Professor of Biostatistics
> University of Auckland
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