int __stdcall NDK_GLM_FORE ( double *  X,
size_t  nVars,
double *  betas,
size_t  nBetas,
double  phi,
WORD  Lvk,
WORD  retType,
double  alpha,
double *  retval 

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

status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
[in] X is the independent variables data matrix, such that each column represents one variable
[in] nVars is the number of independent variables (or columns in X)
[in,out] betas are the coefficients of the GLM model (a one dimensional array)
[in] nBetas is the number of the coefficients in betas. Note that nBetas must be equal to nVars+1
[in,out] phi is the GLM dispersion parameter. Phi is only meaningful for Binomial (1/batch or trial size) and for Gaussian (variance).
Lvk - Binomial : phi = Reciprocal of the batch/trial size.
  • Gaussian : phi = variance.
  • Poisson : phi = 1.0 [in] is the link function that describes how the mean depends on the linear predictor (see GLM_LINK_FUNC).
    1. Identity (default)
    2. Log
    3. Logit
    4. Probit
    5. Complementary log-log
[in] retType is a switch to select the type of value returned: 1= Quick Guess, 2=Calibrated, 3= Std. Errors ( see # FORECAST_RETVAL_FUNC)
[in] alpha is the statistical significance level. If missing, a default of 5% is assumed.
[out] retval is the calculated forecast value
  1. The underlying model is described here.
  2. GLM_FORE returns an array of size equal to number of rows in the input response (Y) or explanatory variables (X).
  3. The number of rows in response variable (Y) must be equal to number of rows of the explanatory variables (X).
  4. 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).
  5. 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.
  6. 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).
  7. For GLM with Gaussian distribution, the dispersion factor (Phi) value must be positive.
Header SFSDK.H
* 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
* D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906
* Box, Jenkins and Reisel; Time Series Analysis: Forecasting and Control; John Wiley & SONS.; 4th edition(Jun 30, 2008), ISBN: 470272848