# NDK_MLR_FORE

 int __stdcall NDK_MLR_FORE ( double ** X, size_t nXSize, size_t nXVars, LPBYTE mask, size_t nMaskLen, double * Y, size_t nYSize, double intercept, double * target, double alpha, WORD nRetType, double * retVal )

Calculates the forecast mean, error and confidence interval.

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] target is the value of the explanatory variables (a one dimensional array). [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=forecast (default), 2=error, 3=upper limit, 4=lower limit): Forecast (mean) Std error Upper limit of the confidence interval Lower limit of the conficence interval [out] retVal is the computed forecast statistics.
Remarks
1. The underlying model is described here.
2. The sample data may include missing values.
3. Each column in the input matrix corresponds to a separate variable.
4. Each row in the input matrix corresponds to an observation.
5. Observations (i.e. row) with missing values in X or Y are removed.
6. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
7. The MLR_FORE function is available starting with version 1.60 APACHE.
Requirements
 Namespace: NumXLAPI Class: SFSDK Scope: Public Lifetime: Static
 int NDK_MLR_FORE ( double[] pXData, size_t nXSize, UIntPtr nXVars, byte[] mask, UIntPtr nMaskLen, double[] pYData, UIntPtr nYSize, double intercept, double target, double alpha, short nRetType, ref double retVal )

Calculates the forecast mean, error and confidence interval.

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 X 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] target is the value of the explanatory variables (a one dimensional array). [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=forecast (default), 2=error, 3=upper limit, 4=lower limit): Forecast (mean) Std error Upper limit of the confidence interval Lower limit of the conficence interval [out] retVal is the computed forecast statistics.
Remarks
1. The underlying model is described here.
2. The sample data may include missing values.
3. Each column in the input matrix corresponds to a separate variable.
4. Each row in the input matrix corresponds to an observation.
5. Observations (i.e. row) with missing values in X or Y are removed.
6. The number of rows of the response variable (Y) must be equal to the number of rows of the explanatory variables (X).
7. The MLR_FORE function is available starting with version 1.60 APACHE.
Exceptions
Exception Type Condition
None N/A
Requirements
Namespace NumXLAPI SFSDK Public Static 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