Calculates the standard deviation (sigma of the error terms (epsilon)) of the GLM model; given the values of explanatory variables.
GLM_FORESD(X, Betas, Phi, Lvk)
- is the independent variables data matrix, such that each column represents one variable.
- are the coefficients of the explanatory variables (a one dimensional array of cells (e.g. rows or columns)).
- is the GLM dispersion paramter.
Distribution PHI Gaussian Variance Poisson 1.0 Binomial Reciprocal of 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)
- 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_FORESD 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.
- The GLM_FORESD is the same as GLM_VOL. It is introduced only for consistency with other models.