| int __stdcall NDK_MLR_PARAM | ( | double ** | X, |
| size_t | nXSize, | ||
| size_t | nXVars, | ||
| LPBYTE | mask, | ||
| size_t | nMaskLen, | ||
| double * | Y, | ||
| size_t | nYSize, | ||
| double | intercept, | ||
| double | alpha, | ||
| WORD | nRetType, | ||
| WORD | nParamIndex, | ||
| double * | retVal | ||
| ) |
Calculates the OLS regression coefficients values.
- 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] 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 the 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] nRetType is a switch to select the return output (1=value (default), 2=std. error, 3=t-stat, 4=P-value, 5=upper limit (CI), 6=lower limit (CI)): - Value (mean)
- Std error
- Test score
- P-value
- Upper limit of the confidence interval
- Lower limit of the confidence interval
[in] nParamIndex is a switch to designate the target parameter (0=intercept (default), 1=first variable, 2=2nd variable, etc.). [out] retVal is the computed statistics of the regression coefficient.
- Remarks
-
- \[ \mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\varepsilon \] \[\hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1} \mathbf{X}^{\rm T}\mathbf{y} = \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i \mathbf{x}^{\rm T}_i \,\big)^{-1} \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i y_i \,\big)\] 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.
- \[ \mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\varepsilon \] \[\hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1} \mathbf{X}^{\rm T}\mathbf{y} = \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i \mathbf{x}^{\rm T}_i \,\big)^{-1} \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i y_i \,\big)\] Where:
- Requirements
-
Header SFSDK.H Library SFSDK.LIB DLL SFSDK.DLL
| Namespace: | NumXLAPI |
| Class: | SFSDK |
| Scope: | Public |
| Lifetime: | Static |
| int NDK_MLR_PARAM | ( | double[] | pXData, |
| double[] | nXSize, | ||
| UIntPtr | nXVars, | ||
| byte[] | mask, | ||
| UIntPtr | nMaskLen, | ||
| double[] | pYData, | ||
| UIntPtr | nYSize, | ||
| double | intercept, | ||
| double | alpha, | ||
| short | nRetType, | ||
| short | ParamIndex, | ||
| ref double | retVal | ||
| ) |
Calculates the OLS regression coefficients values.
- Return Value
-
a value from NDK_RETCODE enumeration for the status of the call.
NDK_SUCCESS operation successful Error Error Code
- 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] 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 the 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] nRetType is a switch to select the return output (1=value (default), 2=std. error, 3=t-stat, 4=P-value, 5=upper limit (CI), 6=lower limit (CI)): - Value (mean)
- Std error
- Test score
- P-value
- Upper limit of the confidence interval
- Lower limit of the confidence interval
[in] nParamIndex is a switch to designate the target parameter (0=intercept (default), 1=first variable, 2=2nd variable, etc.). [out] retVal is the computed statistics of the regression coefficient.
- Remarks
-
- \[ \mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\varepsilon \] \[\hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1} \mathbf{X}^{\rm T}\mathbf{y} = \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i \mathbf{x}^{\rm T}_i \,\big)^{-1} \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i y_i \,\big)\] 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.
- \[ \mathbf{y} = \mathbf{X}\boldsymbol\beta + \boldsymbol\varepsilon \] \[\hat{\boldsymbol\beta} = (\mathbf{X}^{\rm T}\mathbf{X})^{-1} \mathbf{X}^{\rm T}\mathbf{y} = \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i \mathbf{x}^{\rm T}_i \,\big)^{-1} \big(\, \tfrac{1}{n}{\textstyle\sum} \mathbf{x}_i y_i \,\big)\] Where:
- 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
