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    <p><del> I think the most likely cause of this error are negative values or zeros in the data, since the default link in glm.nb is 'log'. It would be easy enough to test by changing <code>link="identity"</code>. </del> I also think you need to try smaller models .... maybe a quarter of those variables to start. That also lets you add related variables as bundles since it looks from the names that you have possibly severe potential for collinearity with categorical variables.</p> <p>We really need a data description. I wondered about <code>Dirty.Industry + Clean.Industry</code>. That is the sort of dichotomy that is better handled with a factor variable that has those levels. That prevents the collinearity if Clean = not-Dirty. Perhaps similarly with your "Heterogeneity" variables. (I'm not convinced that @BenBolker's comment is correct. I think it very possible that you first need statistical consultation before address coding issues.)</p> <pre><code>require(MASS) data(quine) # following example in ?glm.nb page &gt; quine$Days[1] &lt;- -2 &gt; quine.nb1 &lt;- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine, link = "identity") Error in eval(expr, envir, enclos) : negative values not allowed for the 'Poisson' family &gt; quine$Days[1] &lt;- 0 &gt; quine.nb1 &lt;- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine, link = "identity") Error: no valid set of coefficients has been found: please supply starting values In addition: Warning message: In log(y/mu) : NaNs produced </code></pre>
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    1. POR: NA/NaN/Inf in X error
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    1. CONote that a log link is fine for data that is 0. With the link you're modeling the mean response - you aren't applying the link directly to the data itself.
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    2. COI was thinking that the LHS input was part of the "the data".
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    3. COI don't understand what you're saying. In a negative binomial model with any link having a 0 as the response is not an issue. You aren't actually taking the log of the response - you're saying that the log of the expected value has a linear form.
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