Computes the maximum likelihood estimate (MLE) of the model's parameters.
GLM_CALIBRATE(Y, X, Betas, Phi, Lvk, maxIter)
- is the dependent/response variable data set (a one dimensional array of cells (e.g. rows or columns)).
- is the independent variables data matrix, such that each column represents one variable.
- are the initial values of the GLM coefficients (a one dimensional array of cells (e.g. rows or columns)).
- is the GLM dispersion paramter. Phi is only meaningful for Binomial (1/batch or trial size) and for Guassian (variance).
Distribution PHI Gaussian Variance Poisson 1.0 Binomial Reciprocal of the batch/trial size)
- 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).
Link Description 1 Identity (residuals ~ Normal distribution) 2 Log (residuals ~ Poisson distribution) 3 Logit (residuals ~ Binomial distribution) 4 Probit(residuals ~ Binomial distribution) 5 Complementary log-log (residuals ~ Binomial distribution)
- is the maximum number of iterations used to calibrate the model. If missing, the default maximum of 100 is assumed.
- The underlying model is described here.
- Missing values (i.e. #N/A!) are not allowed in the either response (Y) or the explanatory input arrays.
- Number of maximum iteration input must be greater than one.
- The number of rows in response variable (Y) must be equal to number of rows of the explanatory variables (X).
- The betas input is optional, but if the user provide one, the number of betas must equal to the number of explanatory variables (i.e. X) plus one (intercept).
- For GLM with Poisson distribution,
- The values of response variable must be non-negative integers.
- The value of the dispersion factor (Phi) must be either missing or equal to one.
- For GLM with Binomial distribution,
- The values of the response variable must be non-negative fraction between zero and one, inclusive.
- The value of the dispersion factor (Phi) must be a positive fraction (greater than zero, and less than one).
- For GLM with Guassian distribution, the dispersion coefficient (Phi) must be either missing or a positive value.
- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
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