Tool Principal Component Analysis

Principal Component Analysis (PCA) for grids. PCA implementation is based on F.Murtagh's code as provided by the StatLib web site.

References

StatLib Web Site
C-code by F.Murtagh
Bahrenberg, G., Giese, E., Nipper, J. (1992): Statistische Methoden in der Geographie 2 - Multivariate Statistik. pp.198-277.

Author: O.Conrad (c) 2010
Menu: Spatial and Geostatistics|Grids|Principal Components

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Grids	Grid list, input	`GRIDS`	-	-
Input	Eigen Vectors (*)	Table, input, optional	`EIGEN_INPUT`	Use Eigen vectors from this table instead of calculating these from the input grids.	-
Output	Principal Components	Grid list, output	`PCA`	-	-
Output	Eigen Vectors (*)	Table, output, optional	`EIGEN`	Store calculated Eigen vectors to this table, e.g. for later use with forward or inverse PCA.	-
Options	Grid System	Grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Method	Choice	`METHOD`	-	Available Choices: [0] correlation matrix [1] variance-covariance matrix [2] sums-of-squares-and-cross-products matrix Default: 1
	Number of Components	Integer	`COMPONENTS`	number of first components in the output; set to zero to get all	Minimum: 0 Default: 3
	Overwrite Previous Results	Boolean	`OVERWRITE`	-	Default: 1
(*) optional

Command-line

Usage: saga_cmd statistics_grid 8 [-GRIDS <str>] [-PCA <str>] [-EIGEN_INPUT <str>] [-EIGEN <str>] [-METHOD <str>] [-COMPONENTS <num>] [-OVERWRITE <str>]
  -GRIDS:<str>      	Grids
	Grid list, input
  -PCA:<str>        	Principal Components
	Grid list, output
  -EIGEN_INPUT:<str>	Eigen Vectors
	Table, input, optional
  -EIGEN:<str>      	Eigen Vectors
	Table, output, optional
  -METHOD:<str>     	Method
	Choice
	Available Choices:
	[0] correlation matrix
	[1] variance-covariance matrix
	[2] sums-of-squares-and-cross-products matrix
	Default: 1
  -COMPONENTS:<num> 	Number of Components
	Integer
	Minimum: 0
	Default: 3
  -OVERWRITE:<str>  	Overwrite Previous Results
	Boolean
	Default: 1