What is GLM and how does it work?
Learning GLM lets you understand how we can use probability distributions as building blocks for modeling. I assume you are familiar with linear regression and normal distribution. Linear regression is used to predict the value of continuous variable y by the linear combination of explanatory variables X.
What is generalized linear model (GLM)?
In this article, I’d like to explain generalized linear model (GLM), which is a good starting point for learning more advanced statistical modeling. Learning GLM lets you understand how we can use probability distributions as building blocks for modeling. I assume you are familiar with linear regression and normal distribution.
How do I use Poisson regression in GLM?
If you use Python, statsmodels library can be used for GLM. The code for Poisson regression is pretty simple. endog (endogenous) and exog (exogenous) are how you call y and X in statsmodels. Notice you need to add the constant term to X.
How to visualise the uncertainty in a GLM?
You’ve estimated a GLM or a related model (GLMM, GAM, etc.) for your latest paper and, like a good researcher, you want to visualise the model and show the uncertainty in it. In general this is done using confidence intervals with typically 95\% converage.
Is your GLM’s confidence interval wrong?
Well, it’s not! However, the main reason why people mess up computing confidence intervals for a GLM is that they do all the calculations on the response scale.
What happens if the alpha value is false in GLM?
If false and for a fixed alpha value, the next model with the next lambda value out of the lambda array will be built using the coefficients and the GLM state values of the current model. If true, the next GLM model will be built from scratch. The default value is false.