| 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: - standardize ((subtract mean and divide by standard deviation)
- subtract mean.
[in] nCompIndex is the component number to return. [in] retType is a switch to select the return output - proportion of variance,
- variance,
- eigenvalue,
- loadings,
- Principal Component (PC) data.
[out] retVal is the calculated value or data [in] nOutSize is the size of retVal
- Remarks
-
- 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).
- 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 are removed.
- 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: - standardize ((subtract mean and divide by standard deviation)
- subtract mean.
[in] nCompIndex is the component number to return. [in] retType is a switch to select the return output - proportion of variance,
- variance,
- eigenvalue,
- loadings,
- Principal Component (PC) data.
[out] retVal is the calculated value or data [in] nOutSize is the size of retVal
- Remarks
-
- 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).
- 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 are removed.
- 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
