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Can a gamma GLM be used with log link?
A gamma GLM with log link will have the same variance-function assumption (variance proportional to mean squared) as taking logs and fitting a constant variance on that log scale. Other families within the GLM framework will have other variance functions.
How to choose between LM and GLM for a log-transformed?
For your more general question, a good way of focusing on the problem is to consider the difference between LOG.LM (your linear model with the response as log (y)); and LOG.GAUSS.GLM, the glm with the response as y and a log link function. In the first case the model you are fitting is: and in both cases ϵ is distributed N ( 0, σ 2).
Can you use GLM normal distribution with log link function?
Image of the DV distribution on the left and residuals from the GLM normal with log link function on the right. Can I use GLM normal distribution with LOG link function on a DV that has already been log transformed? Is the variance homogeneity test sufficient to justify using normal distribution? Why would equality of variance imply normality?
Is the DV of a GLM a continuous variable?
I have a question concerning Generalized Linear Models (GLM).My dependent variable (DV) is continuous and not normal. So I log transformed it (still not normal but improved it). I want to relate the DV with two categorical variables and one continuous covariable.
How are GLMs used to generalize linear regression?
GLMs generalize linear regression by allowing you to specify a link function and variance structure. A log link with Gaussian errors leads to one regression model. The natural link for Gamma regression is the inverse link, not the log.
How is a GLM different from a mixed model?
There is a huge difference between these models. One need not even appeal to the issue of mixed modeling, consider the GLM case only. GLMs generalize linear regression by allowing you to specify a link function and variance structure. A log link with Gaussian errors leads to one regression model.
When creating a GLMM with gamma distribution do I Need?
When creating a Glmm with Gamma distribution do I need to transform my response variable data to be between 0 and 1? No, you do not need to transform your response variable, y to [ 0, 1]. The only condition to use the Gamma family is that y ∈ ( 0, ∞). You do not mention what software you are using, but here is a little example in R.
Which is the linear predictor of log linked GLM Gaussian?
For log-linked GLM gaussian, log (E (Y)) = Xβ, so E (Y) = eXβ and Y ∼ N (eXβ, σ2). This example is perhaps the cleanest of the three you asked about that will help elucidate the three components. The link is log, the linear predictor is Xβ, and the probability distribution is normal.
How to calculate the variance of a GLM Gaussian?
For GLM gaussian, I assume this has the default identity link, so E ( Y) = X β, then this is no different than a regular linear model with Y ∼ N ( X β, σ 2). Notice this case assumes constant variance as the mean of Y changes linearly with X. For log-linked GLM gaussian, l o g ( E ( Y)) = X β, so E ( Y) = e X β and Y ∼ N ( e X β, σ 2).