Calculates the expected response (i.e. mean) value; given the GLM model and the values of the explanatory variables.

## Syntax

**GLM_MEAN**(

**X**,

**Betas**,

**Phi**,

**Lvk**)

- 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. 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) - 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).
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)

## Remarks

- The underlying model is described here.
- GLM_MEAN 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) value must be either missing or equal to one.

- For GLM with Binomial distribution,
- The values of the response variable 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 positive.

## Files Examples

## Related Links

## References

- 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

## Comments

Article is closed for comments.