NDK_PCR_PARAM

Calculates the regression coefficients values for a given input variable.

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 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]	nRetType	is a switch to select the return output: Value (default), Std. Error t-stat P-Value Upper Limit (CI) Lower Limit (CI))
[in]	nParamIndex	is a switch to designate the target parameter (0 = intercept (default), 1 = first variable, 2 = 2nd variable, etc.).
[out]	retVal	is the calculated parameter value or statistics.

Remarks

1. The underlying model is described here.
\[\mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\varepsilon\] \[\hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1} \mathbf{X}^{\rm T}\mathbf{y} = \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i \mathbf{x}^{\rm T}_i \,\big)^{-1} \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i y_i \,\big).\] Where:

• \(\hat{\boldsymbol\beta}\) is the estimated regression coefficients.

• 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 in X or Y are removed.
• The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
• The MLR_PARAM function is available starting with version 1.60 APACHE.

Requirements

Header	SFSDK.H
Library	SFSDK.LIB
DLL	SFSDK.DLL