Using non-integers vs integers: warnings with non-integers but model won't run with integers
<p>I am having some trouble running negative binomial models. Basically, I have a dataset with counts of animals. However, the effort is different and therefore I can calculate the rate of animals per day. I am doing this with quite a big dataset (>100000 observations). I am quite surprised I couldn't find other topics that covered my question, if you know one: would be helpful!</p>

<p>When trying to fit a model to my data, I run into some problems. Either I run a negative binomial model with the rates </p>

<pre><code>&gt; m1&lt;-glm.nb(Rates ~ Par1+Par2+...+Par7+Par8,data=data) 
</code></pre>

<p>and then I get the following warning messages: </p>

<pre><code>&gt;Warning messages:
1: In dpois(y, mu, log = TRUE) : non-integer x = 25.913718
2: In dpois(y, mu, log = TRUE) : non-integer x = 5.457385
3: In dpois(y, mu, log = TRUE) : non-integer x = 2.195133
4: In dpois(y, mu, log = TRUE) : non-integer x = 2.721088
5: In dpois(y, mu, log = TRUE) : non-integer x = 6.971678
6: In dpois(y, mu, log = TRUE) : non-integer x = 21.863799
7: In dpois(y, mu, log = TRUE) : non-integer x = 5.300733
8: In dpois(y, mu, log = TRUE) : non-integer x = 7.157865
9: In dpois(y, mu, log = TRUE) : non-integer x = 14.117588
10: In dpois(y, mu, log = TRUE) : non-integer x = 6.505993, etc.
</code></pre>

<p>Or I run the model with an offset </p>

<pre><code>&gt; m2&lt;-glm.nb(Count ~ Par1+Par2+...+Par7+Par8+offset(Effort),data=data) 
</code></pre>

<p>This however gives the following error: </p>

<pre><code>&gt; Error: no valid set of coefficients has been found: please supply starting values
In addition: Warning messages:
1: glm.fit: algorithm did not converge 
2: glm.fit: fitted rates numerically 0 occurred 
</code></pre>

<p>I have already tried providing the coefficients of the first model as starting coefficients for the second, but this won't work. Also using the package pscl doesnt work, or increasing the amount of iterations. This is a subset of my data (one species) with very few zeros. </p>

<p>Any suggestions? I feel that actually the second way of modelling this is the proper way of doing it, but I don't know how to get this model to run. Any ideas? Would be much appreciated.</p>