calculates the expected response (i.e. mean) value; given the GLM model and the values of the explanatory variables.
X is the independent variables data matrix, such that each column represents one variable.
Betas are the coefficients of the explanatory variables (a one dimensional array of cells (e.g. rows or columns)).
Phi is the GLM dispersion paramter.
|Binomial||Reciprocal of 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.
- The input argument - Phi - is only meaningful for Binomial (1/batch or trial size) and for Guassian (variance).
- GLM_FORE returns an array of size equal to number of rows in the input response (Y) or explanatory variables (X).
- 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 factor (Phi) value must be positive.