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Results tagged with generalized-linear-model
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user 129321
A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)
18
votes
Can a model for non-negative data with clumping at zeros (Tweedie GLM, zero-inflated GLM, et...
Predicting the proportion of zeros
I am the author of the statmod package and joint author of the tweedie package. Everything in your example is working correctly. The code is accounting correctly fo …
- 13.5k
15
votes
Accepted
Given a GLM using Tweedie, how do I find the coefficients?
Are you familiar with generalized linear models in R? If so, you can fit Tweedie glms just like any other glms.
The glm family definition necessary to make this happen is provided by the statmod R pac …
- 13.5k
13
votes
Why does the glm function converge and not give an error when all y's are equal to the same ...
In some cases, y is equal to the same value (example 1) for all observations. Theoretically, the model should not converge.
Nonsense. This is a very simple dataset for which the maximum likelihood r …
- 13.5k
11
votes
Non normal residuals for Tweedie GLM
No, a Tweedie GLM assumes that the responses follow a Tweedie distribution so, obviously, neither the data nor the ordinary residuals are expected to follow a normal distribution.
No, a Shapiro test …
- 13.5k
10
votes
Accepted
How to correct underdispersion in logistic regression
Getting a residual mean deviance around 0.63 is perfectly normal for binary regression and it does not indicate underdispersion or overdispersion. For binary regression, the residual deviance is deter …
- 13.5k
10
votes
Accepted
Deviance for Gamma GLM
The general derivation of the deviance for a GLM family is given in Section 5.4 of Dunn and Smyth (2018) (the book that you mentioned in a previous post).
You can insert the form of the gamma density …
- 13.5k
10
votes
What are weights in a binary glm and how to calculate them?
I'm going to give a more detailed answer. Tim and Ivan have correctly advised you not to worry about the weights argument and their answers would be excellent for continuous GLMs. Binary regression wi …
- 13.5k
10
votes
Accepted
Tweedie Dispersion Parameter Estimation Methods
Tweedie generalized linear models assume a mean-variance relationship with variance power $p$, defined by
$$E(y_i)=\mu_i$$
and
$${\rm var}(y_i)=\phi \mu_i^p$$
where $y_i$ is the $i$th observation, $\m …
- 13.5k
9
votes
Accepted
In GLMs are the Scale and Dispersion parameters the same?
Short Answer
Yes, in generalized linear model (GLM) theory, you can assume that the "dispersion" parameter and the "scale" parameter are the same thing. Roughly speaking, "scale" is the older GLIM te …
- 13.5k
9
votes
Accepted
The difference of Standard Error between glm(y~x, family=poisson(link=identity)) and optim()...
In statistical likelihood theory, minus the second derivative of the log-likelihood function is called the observed information. We might write this as
$$
I = -\ddot \ell(y; \theta)
$$
where the dots …
- 13.5k
8
votes
Accepted
Conditionally heteroskedastic linear regression: How can I model variance from given predict...
Simultaneous modelling of mean and variance using double generalized linear models
The emphasis of gamlss is obviously on generalized additive models (GAMs). The general of idea of simultaneously mod …
- 13.5k
8
votes
Accepted
Small Sample Sizes and Zero Inflated Count Data in R
The problem you are observing with lack of significance of GA has nothing to do with zero inflation or with random effects.
It is simply a limitation of Wald tests for count models.
If you replace the …
- 13.5k
7
votes
Accepted
Overdispersion in fitted generalized linear model with insignificant regression coefficients
Yes, that is true.
There are only two commonly-used generalized linear model families for which the concept of overdispersion is relevant. These are Poisson regression or binomial regression when the …
- 13.5k
7
votes
Accepted
Canonical link of Gamma Distribution
Yes, you are quite right. When we write the gamma distribution as an exponential dispersion model
$$f(y;\mu,\phi)=a(y,\phi)\exp\big\{\frac1{\phi}(y\theta-\kappa(\theta))\big\}$$
we do find that the ca …
- 13.5k
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 a …
- 13.5k