From: Rubén Roa-Ureta <rroa_at_udec.cl>

Date: Mon, 21 Apr 2008 15:43:09 -0400

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 21 Apr 2008 - 19:53:47 GMT

Date: Mon, 21 Apr 2008 15:43:09 -0400

andrea previtali wrote:

*> Hi,
*

> I need to analyze the influences of several factors on a variable that is a measure of fecundity, consisting of 73 observations ranging from 0 to 5. The

*> variable is continuous and highly positive skewed, none of the typical
**> transformations was able to normalize the data. Thus, I was thinking in analyzing these data using a generalized linear model where I
**> can specify a distribution other than normal. I'm thinking it may fit a
**> gamma or exponential distribution. But I'm not sure if the data meets
**> the assumptions of those distributions because their definitions are
**> too complex for my understanding!
*

Roughly, the exponential distribution is the model of a random variable
describing the time/distance between two independent events that occur
at the same constant rate. The gamma distribution is the model of a
random variable that can be thought of as the sum of exponential random
variables. I don't think fecundity data, the count of reproductive
cells, qualifies as a random variable to be modeled by either of these
distributions. If the count of reproductive cells is very large, and you
are modeling this count as a function of animal size, such as length,
you should consider the lognormal distribution, since the count of cells
grow multiplicatively (volumetrically) with the increase in length. In
that case you can model your response variable using glm with
family=gaussian(link="log").

Rubén

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 21 Apr 2008 - 19:53:47 GMT

Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Australia.

Archive generated by hypermail 2.2.0, at Mon 21 Apr 2008 - 20:30:33 GMT.

*
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help.
Please read the posting
guide before posting to the list.
*