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What is the difference between glm and GAM?
The main difference imho is that while “classical” forms of linear, or generalized linear, models assume a fixed linear or some other parametric form of the relationship between the dependent variable and the covariates, GAM do not assume a priori any specific form of this relationship, and can be used to reveal and …
What is the difference between glm and linear regression?
General Linear Models refers to normal linear regression models with a continuous response variable. General Linear Models assumes the residuals/errors follow a normal distribution. Generalized Linear Model, on the other hand, allows residuals to have other distributions from the exponential family of distributions.
What’s the difference between a GLM model and Gee model?
Whats the difference between a GLM model (logistic regression) with a binary response variable which includes subject and time as covariates and the analogous GEE model which takes into account correlation between measurements at multiple time points? with logit link function.
When to use a GAM instead of a GLM?
Someone recently told me that GAMs should only be used when I assume the data structure to be “additive”, i.e. I expect additions of x to predict y. Another person pointed out that a GAM does a different type of regression analysis than a GLM, and that a GLM is preferred when linearity can be assumed.
How is a GLM used in a logistic regression model?
If y is a count of something, such as the number of coffees someone drinks on a certain day, we could model it with a GLM with a Poisson distribution and the natural logarithm as the link function: The logistic regression model is also a GLM that assumes a Bernoulli distribution and uses the logit function as the link function.
How is the GLM framework used in machine learning?
Under the GLM framework, this concept generalizes to any distribution (from the exponential family) and arbitrary link functions. If y is a count of something, such as the number of coffees someone drinks on a certain day, we could model it with a GLM with a Poisson distribution and the natural logarithm as the link function: