NDK_PCA_COMP

int __stdcall NDK_PCA_COMP ( double **  X,
size_t  nXSize,
size_t  nXVars,
LPBYTE  mask,
size_t  nMaskLen,
WORD  standardize,
WORD  nCompIndex,
WORD  retType,
double *  retVal,
size_t  nOutSize 
)

Returns an array of cells for the i-th principal component (or residuals).

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
[in] standardize is a flag or switch to standardize the input variables prior to the analysis:
  1. standardize ((subtract mean and divide by standard deviation)
  2. subtract mean.
[in] nCompIndex is the component number to return.
[in] retType is a switch to select the return output
  1. proportion of variance,
  2. variance,
  3. eigenvalue,
  4. loadings,
  5. Principal Component (PC) data.
[out] retVal is the calculated value or data
[in] nOutSize is the size of retVal
Remarks
  1. The PCA_COMP function must be entered as an array formula (for return-types greater than 3) in a range that has the rows as the number of variables (return-type = 4) or the number of observations (return-type = 5).
  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 are removed.
  6. The PC_COMP 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_PCA_COMP ( double[]  pXData,
UIntPtr  nXSize,
UIntPtr  nXVars,
byte[]  mask,
UIntPtr  nMaskLen,
short  standardize,
short  nCompIndex,
short  retType,
double[]  retVal,
UIntPtr  nOutSize 
)

Returns an array of cells for the i-th principal component (or residuals).

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 variables data matrix, such that each column represents one variable
[in] nXSize is the number of observations (i.e. rows) in pXData
[in] nXVars is the number of variables (i.e. columns) in pXData
[in] mask is the boolean array to select a subset of the input variables in pXData. If missing (i.e. NULL), all variables in pXData are included.
[in] nMaskLen is the number of elements in
[in] standardize is a flag or switch to standardize the input variables prior to the analysis:
  1. standardize ((subtract mean and divide by standard deviation)
  2. subtract mean.
[in] nCompIndex is the component number to return.
[in] retType is a switch to select the return output
  1. proportion of variance,
  2. variance,
  3. eigenvalue,
  4. loadings,
  5. Principal Component (PC) data.
[out] retVal is the calculated value or data
[in] nOutSize is the size of retVal
Remarks
  1. The PCA_COMP function must be entered as an array formula (for return-types greater than 3) in a range that has the rows as the number of variables (return-type = 4) or the number of observations (return-type = 5).
  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 are removed.
  6. The PC_COMP 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