# Tag Info

### How to implement a mixed model using betareg function in R?

The package glmmTMB may be helpful for anyone with a similar question. For example, if you wanted to include pond from the above question as a random effect, the following code would do the trick: <...
• 406
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### Why Beta/Dirichlet Regression are not considered Generalized Linear Models?

Check the original reference: Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799-815. as the authors note, ...
• 140k
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### Why exactly can't beta regression deal with 0s and 1s in the response variable?

Because the loglikelihood contains both $\log(x)$ and $\log(1-x)$, which are unbounded when $x=0$ or $x=1$. See equation (4) of Smithson & Verkuilen, "A Better Lemon Squeezer? Maximum-Likelihood ...
• 599
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### How to implement a mixed model using betareg function in R?

The current capabilities of betareg do not include random/mixed effects. In betareg() you can only include fixed effect, e.g., ...
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### Why use the logit link in beta regression?

Justification of the link function: A link function $g(\mu): (0,1) \rightarrow \mathbb{R}$ assures that all fitted values $\hat \mu = g^{-1}(x^\top \hat \beta)$ are always in $(0, 1)$. This may not ...
• 15.8k
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### interpretation of betareg coef

Yes, the logit link can be interpreted like that. It's just not a change in "odds" (= ratio of probabilities) but a change in a ratio of proportions. More formally, the model equation for the ...
• 15.8k
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### Regression Bounded Between -1 and 1

You can always use beta regression (Ferrari and Cribari-Neto, 2004). It's a model for response variable bounded in $(0, 1)$, but you can easily transform your variable by taking $\frac{Y+1}{2}$ (I ...
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### Why use the logit link in beta regression?

It is incorrect that the logistic regression can only be used to model binary outcome data. The logistic regression model is appropriate for any data where 1) the expected value of outcome follows a ...
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### Proportion data with number of trials known (and separation?): GLM or beta regression?

The data you have are really a classic binomial setting and you can use binomial GLMs to model the data, either using the standard maximum likelihood (ML) estimator or using a bias-reduced (BR) ...
• 15.8k
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### What is the difference between beta regression and quasi glm with variance = $\mu(1-\mu)$?

You're correct that the mean and variance functions are of the same form. This suggests that in very large samples, as long as you don't have observations really close to 1 or 0 they should tend to ...
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### variance vs. standard deviation in beta regression

I think there are two potential sources of confusion here: (1) What the variance pertains to. (2) What kind of intervals are computed. The variance is the predicted variance of the response and not ...
• 15.8k
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### Zero inflated beta regression using gamlss for vegetation cover data

I have added preliminary support for gamlss to the emmeans package... ...
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### Why is betareg() giving "invalid dependent variable" error?

The error message posted by R tells you what the problem is: invalid dependent variable, all observations must be in (0,1) In other words, your dependent variable should take values that are strictly ...
• 20.6k
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### Beta regression shows a weird plot

Your regression has two inputs crop coverage ~ soil_moisture + weed_coverage but you've only plotted crop_coverage against ...
• 2,917
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### Logit transformation or beta regression for proportion data

They mean that once you transformed your dependent variable (e.g., from $y$ to ${\rm logit}(y)$), the parameters of the regression model tell you how independent variables affect ${\rm logit}(y)$, ...
• 4,091
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### How do you report results from a Beta Regression (R output)?

The beta regression model can have two submodels: (1) a regression model for the mean - similar to a linear regression model or a binary regression model; (2) a regression model for the precision ...
• 15.8k

### Dealing with 0,1 values in a beta regression

I think the actual "correct" answer to this question is zero-one inflated beta regression. This is designed to handle data that vary continuously on the interval [0,1], and allows many real 0's and 1'...
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### Why Beta/Dirichlet Regression are not considered Generalized Linear Models?

The answer by @probabilityislogic is on the right track. The beta distribution is in the two parameter exponential family. The simple GLM models described by Nelder and Wedderburn (1972) do not ...
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### Why are my beta regression results biased?

The parameterization you use is not the same as in betareg hence you cannot recover your original parameters. The rbeta() ...
• 15.8k

### Proportion data with number of trials known (and separation?): GLM or beta regression?

It isn't that beta regression on its own solved the problem. It's that you adjusted the data with a line of code: ...
• 95.5k
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### Modeling a 0-1 Bounded Dependent Variable That is Not a Proportion/Probability

Beta regression still makes perfect sense. It is in no way limited to, or even mainly motivated by, data that are probabilities or proportions. (If your data are "far" away from the ...
• 127k