NDK_COLNRTY_TEST

C/C++
.Net

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
	)