# Tag Info

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### Negative binomial distribution vs binomial distribution

The difference is what we are interested in. Both distributions are built from independent Bernoulli trials with fixed probability of success, p. With the Binomial distribution, the random variable X ...
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### Diagnostics for generalized linear (mixed) models (specifically residuals)

This answer is not based on my knowledge but rather quotes what Bolker et al. (2009) wrote in an influential paper in the journal Trends in Ecology and Evolution. Since the article is not open access (...
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### Continuous generalization of the negative binomial distribution

That's an interesting question. My research group has been using the distribution you refer to for some years in our publicly available bioinformatics software. As far as I know, the distribution does ...
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### Expected number of times to roll a die until each side has appeared 3 times

Suppose all $d=6$ sides have equal chances. Let's generalize and find the expected number of rolls needed until side $1$ has appeared $n_1$ times, side $2$ has appeared $n_2$ times, ..., and side $d$ ...
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### How to deal with overdispersion in Poisson regression: quasi-likelihood, negative binomial GLM, or subject-level random effect?

Poisson regression is just a GLM: People often speak of the parametric rationale for applying Poisson regression. In fact, Poisson regression is just a GLM. That means Poisson regression is justified ...

### What is the appropriate model for underdispersed count data?

The best --- and standard ways to handle underdispersed Poisson data is by using a generalized Poisson, or perhaps a hurdle model. Three parameter count models can also be used for underdispersed data;...

### Negative binomial distribution vs binomial distribution

Negative binomial distribution, despite seemingly obvious relation to binomial, is actually better compared against the Poisson distribution. All three are discrete, btw. In practical applications, ...

### Interpretation of .L & .Q output from a negative binomial GLM with categorical data

Your variables aren't just coded as factors (to make them categorical), they are coded as ordered factors. Then, by default, R fits a series of polynomial functions to the levels of the variable. ...

### Diagnostic plots for count regression

This is an old question, but I thought it would be useful to add that my DHARMa R package (available from CRAN, see here) now provides standardized residuals for GLMs and GLMMs, based on a simulation ...
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