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):
  1. Forecast (mean)
  2. Std error
  3. Upper limit of the confidence interval
  4. 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
Header SFSDK.H
Library SFSDK.LIB
DLL SFSDK.DLL
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):
  1. Forecast (mean)
  2. Std error
  3. Upper limit of the confidence interval
  4. 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
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