The generalized linear model (GLM) is an extension of the linear regression model that enables the linear equation to relate to the dependent variables by a link function. The GLM function supports several distribution families and associated link functions.
You can input the output table to the function GLMPredict_MLE (ML Engine).
The GLM function implementation uses the Fisher Scoring Algorithm, which scales better than the least-squares algorithm that the glm() function in the R package stats uses. The results of the two algorithms usually match closely. However, when the input data is highly skewed or has a large variance, the Fisher Scoring Algorithm can diverge, and you must use data set knowledge and trial and error to select the optimal family and link functions.
If the predictors are collinear, Teradata recommends using GLML1L2 with regularization parameters.
- Dobson, A.J.; Barnett, A.G. (2008). Introduction to Generalized Linear Models (3rd ed.). Boca Raton, FL: Chapman and Hall/CRC. ISBN 1-58488-165-8.
- Hardin, James; Hilbe, Joseph (2007). Generalized Linear Models and Extensions (2nd ed.). College Station: Stata Press. ISBN 1-59718-014-9.