GLM - GLM Model Definition

Returns an array of cells for the packed form of a given GLM model.


GLM (Betas, Phi, Lvk)

are the coefficients of the GLM model (a one-dimensional array of cells (e.g., rows or columns)).
is the GLM dispersion paramter. This argument is only required for Binomial distribution (phi = 1/batch size) and Gaussian (phi = sigma). 
is the link function that describes how the mean depends on the linear predictor (1 = Identity (default), 2 = Log, 3 = Logit, 4 = Probit, 5 = Log-Log).
Value Lvk
1 Identity (Residuals ~ Normal distribution) (default).
2 Log (Residuals ~ Poisson distribution).
3 Logit (Residuals ~ Binomial distribution).
4 Probit (Residuals ~ Binomial distribution).
5 Complementary log-log (Residuals ~ Binomial distribution).


  1. The underlying model is described here.
  2. John Nelder and Robert Wedderburn formulated generalized linear models as a way of unifying various other statistical models, including linear regression, logistic regression, and Poisson regression, under one framework.

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