28
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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
24
votes
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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, ...
Tim♦
- 135k
21
votes
Accepted
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 ...
- 559
15
votes
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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., ...
- 15.2k
15
votes
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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 ...
- 15.2k
14
votes
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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 ...
- 15.2k
12
votes
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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 ...
Tim♦
- 135k
12
votes
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 ...
- 60.4k
11
votes
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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 ...
- 277k
11
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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 ...
- 19.7k
11
votes
Accepted
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) ...
- 15.2k
10
votes
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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 ...
- 15.2k
10
votes
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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...
...
- 19.3k
10
votes
Accepted
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 ...
- 2,808
9
votes
Accepted
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 ...
- 15.2k
9
votes
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 ...
- 71.5k
9
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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 ${\rm logit}(y)$, ...
- 3,961
8
votes
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:
...
- 86.5k
7
votes
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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() ...
- 15.2k
7
votes
Why does my beta regression look linear when plotted?
The first thing I notice is that the relationship between the input and output is approximately linear, so from an empirical point of view I'd expect the fitted model to replicate this relationship.
...
- 2,808
7
votes
Can the Beta-regression be written in the GLM form?
GLMs assume that the response distribution is an Exponential Dispersion Model (EDM):
$$y_i \sim \mbox{ED}(\mu_i,\phi/w_i)$$
where $\phi$ is the dispersion parameter and $w_i$ is a known weight.
EDMs ...
- 12.1k
6
votes
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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6
votes
Variance-covariance matrix of the errors in linear regression
OLS estimation of the error variance, $\sigma^2$:
$$s^2=\frac{\hat \varepsilon^\top\hat \varepsilon}{n-p}$$
This is included in Practical Regression and Anova using R
by Julian J. Faraway, page ...
- 25.7k
6
votes
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Regression when both the predictor and outcome variables are proportions
A glm with a binomial distribution and a logit link should work fine. If there are no probabilities of $0$ or $1$, beta regression is another possibility. In this case, they yield almost ...
- 29.4k
6
votes
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Interpretation of the beta regression coefficients with logit link used to analyse percentage 0-100%
I assume that you have scaled your percentages to values in [0,1] (or in the open interval (0,1) after you've added/subtracted 0.001; you might want to consider adjusting by smaller values, as values ...
- 41.4k
6
votes
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Which link function could be used for a glm where the response is per cent (0 - 100%)?
Nice question!
You can start your modelling efforts by converting your cover variable so that it is expressed as a proportion rather than a percentage (i.e., simply divide the values of your original ...
- 19.7k
6
votes
Why does my beta regression look linear when plotted?
When the outcomes are very close to 0.5, the model on the response scale is nearly linear. The plot below demonstrates this. In black, I have plotted the inverse of the logit function (which is what ...
- 33.4k
6
votes
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Modeling the precision and location parameters in beta regression
I wouldn't be aware of any such reason.
I think it's a reasonable default to assume that all predictor variables that affect the mean $\mu$ of the response variable actually affect its entire ...
- 15.2k
5
votes
How do I find the equation of a predicted beta regression curve?
The answer depends on what exactly you want to predict and whether you want to do it by hand or using betareg. Generally, I would recommend to read ...
- 15.2k
5
votes
Accepted
Appropriate GLM for proportion response variable
First, you should incorporate the number of trials in the model. I illustrate below using what I feel is a more elegant way to obtain the fractions, and creating new variables ...
- 19.3k
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