NDK_COLNRTY_TEST

int __stdcall NDK_COLNRTY_TEST ( double **  XX,
size_t  N,
size_t  M,
LPBYTE  mask,
size_t  nMaskLen,
COLNRTY_TEST_TYPE  nMethod,
WORD  nColIndex,
double *  retVal 
)

Returns the collinearity test statistics for a set of input variables.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] XX is the input variables matrix data (two dimensional).
[in] N is the number of rows (observations) in XX.
[in] M is the number of columns (variables) in XX.
[in] mask is the boolean array to select a subset of the input variables in X. If NULL, all variables in X are included.
[in] nMaskLen is the number of elements in the mask. Must be zero or equal to M.
[in] nMethod is the multi-colinearity measure to compute
Method Value Description
COLNRTY_CN 1 Condition Number.
COLNRTY_VIF 2 Variation Inflation Factor (VIF)
COLNRTY_DET 3 Determinant
COLNRTY_EIGEN 4 Eigenvalues
[in] nColIndex is a switch to designate the explanatory variable to examine (not required for condition number).
[out] retVal is the calculated statistics of collinearity.
Remarks
  • The sample data may include missing values.
  • Each column in the input matrix corresponds to a separate variable.
  • Each row in the input matrix corresponds to an observation.
  • Observations (i.e. row) with missing values are removed.
  • In the variance inflation factor (VIF) method, a series of regressions models are constructed, where one variable is the dependent variable against the remaining predictors.
  • \[\textrm{Tolerance}_i = 1-R_i^2\] \[\textrm{VIF}_i =\frac{1}{\textrm{Tolearance}_i} = \frac{1}{1-R_i^2}\] Where:
    • \(R_i^2\) is the coefficient of determination of a regression of explanator \(i\) on all the other explanators.
  • A tolerance of less than 0.20 or 0.10 and/or a VIF of 5 or 10 and above indicates a multicollinearity problem.
  • The condition number (\(\kappa\)) test is a standard measure of ill-conditioning in a matrix; It will indicate that the inversion of the matrix is numerically unstable with finite-precision numbers (standard computer floats and doubles).
  • \[ X = \begin{bmatrix} 1 & X_{11} & \cdots & X_{k1} \\ \vdots & \vdots & & \vdots \\ 1 & X_{1N} & \cdots & X_{kN} \end{bmatrix} \] \[\kappa = \sqrt{\frac{\lambda_{max}}{\lambda_{min}}}\] Where:
    • \(\lambda_{max}\) is the maximum eigenvalue.
    • \(\lambda_{min}\) is the minimum eigenvalue.
  • As a rule of thumb, a condition number ($\kappa$) greater or equal to 30 indicates a severe multi-collinearity problem.
  • The CollinearityTest function is available starting with version 1.60 APACHE.
Requirements
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
Examples


   
Namespace:  NumXLAPI
Class:  SFSDK
Scope:  Public
Lifetime:  Static
int NDK_COLNRTY_TEST ( ref UIntPtr  pData,
UIntPtr  nSize,
UIntPtr  nVars,
Byte[]  mask,
UIntPtr  nMaskLen,
COLNRTY_TEST_TYPE  nMethod,
short  nColIndex,
ref double  retVal 
)

Returns the collinearity test statistics for a set of input variables.

Returns
status code of the operation
Return values
NDK_SUCCESS  Operation successful
NDK_FAILED  Operation unsuccessful. See Macros for full list.
Parameters
[in] XX is the input variables matrix data (two dimensional).
[in] N is the number of rows (observations) in XX.
[in] M is the number of columns (variables) in XX.
[in] mask is the boolean array to select a subset of the input variables in X. If NULL, all variables in X are included.
[in] nMaskLen is the number of elements in the mask. Must be zero or equal to M.
[in] nMethod is the multi-colinearity measure to compute
Method Value Description
COLNRTY_CN 1 Condition Number.
COLNRTY_VIF 2 Variation Inflation Factor (VIF)
COLNRTY_DET 3 Determinant
COLNRTY_EIGEN 4 Eigenvalues
[in] nColIndex is a switch to designate the explanatory variable to examine (not required for condition number).
[out] retVal is the calculated statistics of collinearity.
Remarks
  • The sample data may include missing values.
  • Each column in the input matrix corresponds to a separate variable.
  • Each row in the input matrix corresponds to an observation.
  • Observations (i.e. row) with missing values are removed.
  • In the variance inflation factor (VIF) method, a series of regressions models are constructed, where one variable is the dependent variable against the remaining predictors.
  • \[\textrm{Tolerance}_i = 1-R_i^2\] \[\textrm{VIF}_i =\frac{1}{\textrm{Tolearance}_i} = \frac{1}{1-R_i^2}\] Where:
    • \(R_i^2\) is the coefficient of determination of a regression of explanator \(i\) on all the other explanators.
  • A tolerance of less than 0.20 or 0.10 and/or a VIF of 5 or 10 and above indicates a multicollinearity problem.
  • The condition number (\(\kappa\)) test is a standard measure of ill-conditioning in a matrix; It will indicate that the inversion of the matrix is numerically unstable with finite-precision numbers (standard computer floats and doubles).
  • \[ X = \begin{bmatrix} 1 & X_{11} & \cdots & X_{k1} \\ \vdots & \vdots & & \vdots \\ 1 & X_{1N} & \cdots & X_{kN} \end{bmatrix} \] \[\kappa = \sqrt{\frac{\lambda_{max}}{\lambda_{min}}}\] Where:
    • \(\lambda_{max}\) is the maximum eigenvalue.
    • \(\lambda_{min}\) is the minimum eigenvalue.
  • As a rule of thumb, a condition number ($\kappa$) greater or equal to 30 indicates a severe multi-collinearity problem.
  • The CollinearityTest 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