Returns an array of cells for the initial (non-optimal) guess of the model's parameters.
Y is the response or the dependent variable data array (one dimensional array of cells (e.g. rows or columns)).
X is the independent variables data matrix, such that each column represents one variable.
Phi is the GLM dispersion paramter. Phi is only meaningful for Binomial (1/batch or trial size) and for Guassian (variance).
|Binomial||Reciprocal of the batch/trial size)|
Lvk 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).
|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)|
- The underlying model is described here.
- GLM_GUESS returns an array of size equal number of betas plus one (Phi).
- The number of rows in response variable (Y) must be equal to number of rows of the explanatory variables (X).
- 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 varaible must be non-negative fractions 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 factor (Phi) value must be either missing or positive.