int __stdcall NDK_PCR_STEPWISE ( double **  X,
size_t  nXSize,
size_t  nXVars,
LPBYTE  mask,
size_t  nMaskLen,
double *  Y,
size_t  nYSize,
double  intercept,
double  alpha,
WORD  nMode 

Returns an array of cells for the i-th principal component (or residuals).

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] nXSize is the number of observations (i.e. rows) in X
[in] nXVars is the number of variables (i.e. columns) in X
[in] mask is the boolean array to select a subset of the input variables in X. If missing (i.e. NULL), all variables in X are included.
[in] nMaskLen is the number of elements in mask
[in] Y is the response or the dependent variable data array (one dimensional array)
[in] nYSize is the number of elements in Y
[in] intercept is the constant or the intercept value to fix (e.g. zero). If missing (NaN), an intercept will not be fixed and is computed normally
[in] alpha is the statistical significance of the test (i.e. alpha)
[in] nMode is a switch to select the variable's inclusion/exclusion approach (1=forward selection (default), 2=backward elimination , 3=bi-directional elimination):
  1. Forward selection
  2. Backward elimination
  3. Bi-directional elimination
  1. The underlying model is described here.
  2. The stepwise regression includes regression models in which the choice of predictive variables is carried out by an automatic procedure. The procedure takes the form of a sequence of f-tests in selecting or eliminating explanatory variables.
  3. The three main approaches are:
    • Forward Selection, which involves starting with no variables in the model, testing the addition of each variable using a chosen model comparison criterion, adding the variable (if any) that improves the model the most, and repeating this process until no further improvement is possible.
    • Backward Elimination, which involves starting with all candidate variables, testing the deletion of each variable using a chosen model comparison criterion, deleting the variable (if any) that improves the model the most by being deleted, and repeating this process until no further improvement is possible.
    • Bi-directional Elimination, a combination of the above tests, involves testing at each step for variables to be included or excluded.
  4. The initial values in the mask array define the variables set that MLR_STEPWISE works with. In other words, variables which are not selected will not be considered during the regression.
  5. The sample data may include missing values.
  6. Each column in the input matrix corresponds to a separate variable.
  7. Each row in the input matrix corresponds to an observation.
  8. Observations (i.e. row) with missing values in X or Y are removed.
  9. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
  10. The MLR_STEPWISE function is available starting with version 1.60 APACHE.
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