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r - GLM with Cauchy family - Stack Overflow There are two reasons you can't do this with a GLM (specifically, with glm()) In the narrow sense, GLMs are only defined for conditional distributions in the exponential family (Poisson, binomial, Gaussian, Gamma, and a few others)
Linear regression with Cauchy distribution for errors Rather than choose between the Normal and the Cauchy distribution for the errors, let's fit a simple linear regression with t t -distributed errors And we will use theory to explore what degrees of freedom, ν ν, are most consistent with the data
R: Bayesian generalized linear models. The program is a simple alteration of glm() that uses an approximate EM algorithm to update the betas at each step using an augmented regression to represent the prior information
Generalized Linear Models in R - Stanford University The models are fit using iterative reweighted least squares, so it also possible to set convergence parameters It is also possible to include an offset term in the formula, using the offset() argument in the formula
R: Fitting Generalized Linear Models - UCLA Mathematics Description glm is used to fit generalized linear models Models for glm are specified by giving a symbolic description of the linear predictor and a description of the error distribution
r - Fit distributions with glm - Cross Validated Is there a way to fit it with the glm families? Update: The data comes from sales orders, but it is always grater than 0, that's why I can use the exponential or gamma distributions