NDK_MLR_STEPWISE

Last Modified on 05/05/2016 12:28 pm CDT
int __stdcall NDK_MLR_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 a list of the selected variables after performing the stepwise regression.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] X is the independent (explanatory) variables data matrix, such that each column represents one variable.
[in] nXSize is the number of observations (rows) in X.
[in] nXVars is the number of independent (explanatory) variables (columns) in X.
[in,out] mask is the boolean array to choose the explanatory variables in the model. If missing, all variables in X are included.
[in] nMaskLen is the number of elements in the "mask."
[in] Y is the response or dependent variable data array (one dimensional array of cells).
[in] nYSize is the number of observations in Y.
[in] intercept is the constant or intercept value to fix (e.g. zero). If missing (i.e. NaN), an intercept will not be fixed and is computed normally.
[in] alpha is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.
[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. Bacward elemination
  3. Bi-directional elemination
Remarks
  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 additional variables improve the model.
    • 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.
    • Bidirectional Elimination a combination of the above tests, involves testing at each step for variables to be included or excluded.
  4. One of the main issues with stepwise regression is that it searches a large space of possible models. Hence it is prone to overfitting the data.
  5. 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.
  6. The sample data may include missing values.
  7. Each column in the input matrix corresponds to a separate variable.
  8. Each row in the input matrix corresponds to an observation.
  9. Observations (i.e. row) with missing values in X or Y are removed.
  10. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
  11. The MLR_STEPWISE function is available starting with version 1.60 APACHE.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_MLR_STEPWISE ( double **  pXData,
UIntPtr  nXSize,
UIntPtr  nXVars,
byte[]  mask1,
byte[]  mask2,
UIntPtr  nMaskLen,
double[]  pYData,
UIntPtr  nYSize,
double  intercept,
double  alpha,
short  nRetType, 
ref double  retVal
)

Returns a list of the selected variables after performing the stepwise regression.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] pXData is the independent (explanatory) variables data matrix, such that each column represents one variable.
[in] nXSize is the number of observations (rows) in pXData.
[in] nXVars is the number of independent (explanatory) variables (columns) in pXData.
[in,out] mask is the boolean array to choose the explanatory variables in the model. If missing, all variables in pXData are included.
[in] nMaskLen is the number of elements in the "mask."
[in] pYData is the response or dependent variable data array (one dimensional array of cells).
[in] nYSize is the number of observations in pYData.
[in] intercept is the constant or intercept value to fix (e.g. zero). If missing (i.e. NaN), an intercept will not be fixed and is computed normally.
[in] alpha is the statistical significance of the test (i.e. alpha). If missing or omitted, an alpha value of 5% is assumed.
[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. Bacward elemination
  3. Bi-directional elemination
Remarks
  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 additional variables improve the model.
    • 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.
    • Bidirectional Elimination a combination of the above tests, involves testing at each step for variables to be included or excluded.
  4. One of the main issues with stepwise regression is that it searches a large space of possible models. Hence it is prone to overfitting the data.
  5. 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.
  6. The sample data may include missing values.
  7. Each column in the input matrix corresponds to a separate variable.
  8. Each row in the input matrix corresponds to an observation.
  9. Observations (i.e. row) with missing values in X or Y are removed.
  10. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
  11. The MLR_STEPWISE function is available starting with version 1.60 APACHE.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI
Class SFSDK
Scope Public
Lifetime Static
Package NumXLAPI.DLL
Examples

	












	
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 


* 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






	

    

   		
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