Multiple Regression Analysis (Points and Predictor Grids)

Author: O.Conrad (c) 2004
Menu: Spatial and Geostatistics | Regression

Description

Linear regression analysis of point attributes with multiple grids. Details of the regression/correlation analysis will be saved to a table. The regression function is used to create a new grid with regression based values. The multiple regression analysis uses a forward selection procedure.

References

Bahrenberg, G., Giese, E., Nipper, J. (1992): Statistische Methoden in der Geographie 2 - Multivariate Statistik. Stuttgart, 415p.

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Predictors	grid list, input	`PREDICTORS`	-	-
Input	Points	shapes, input	`POINTS`	-	-
Output	Details: Coefficients	table, output, optional	`INFO_COEFF`	-	-
	Details: Model	table, output, optional	`INFO_MODEL`	-	-
	Details: Steps	table, output, optional	`INFO_STEPS`	-	-
	Residuals	shapes, output, optional	`RESIDUALS`	-	-
	Regression	grid, output	`REGRESSION`	regression model applied to predictor grids	-
	Regression with Residual Correction	grid, output, optional	`REGRESCORR`	regression model applied to predictor grids with interpolated residuals added	-
Options	Grid System	grid system	`PARAMETERS_GRID_SYSTEM`	-	-
	Dependent Variable	table field	`ATTRIBUTE`	-	-
	Resampling	choice	`RESAMPLING`	-	Available Choices: [0] Nearest Neighbour [1] Bilinear Interpolation [2] Bicubic Spline Interpolation [3] B-Spline Interpolation Default: 3
	Include X Coordinate	boolean	`COORD_X`	-	Default: 0
	Include Y Coordinate	boolean	`COORD_Y`	-	Default: 0
	Intercept	boolean	`INTERCEPT`	-	Default: 1
	Method	choice	`METHOD`	-	Available Choices: [0] include all [1] forward [2] backward [3] stepwise Default: 3
	Significance Level	floating point number	`P_VALUE`	Significance level (aka p-value) as threshold for automated predictor selection, given as percentage	Minimum: 0.000000 Maximum: 100.000000 Default: 5.000000
	Cross Validation	choice	`CROSSVAL`	-	Available Choices: [0] none [1] leave one out [2] 2-fold [3] k-fold Default: 0
	Cross Validation Subsamples	integer number	`CROSSVAL_K`	number of subsamples for k-fold cross validation	Minimum: 2 Default: 10
	Residual Interpolation	choice	`RESIDUAL_COR`	-	Available Choices: [0] Multilevel B-Spline Interpolation [1] Inverse Distance Weighted Default: 0

Command Line

Usage: saga_cmd statistics_regression 1 [-PREDICTORS ] [-POINTS ] [-ATTRIBUTE ] [-INFO_COEFF ] [-INFO_MODEL ] [-INFO_STEPS ] [-RESIDUALS ] [-REGRESSION ] [-REGRESCORR ] [-RESAMPLING ] [-COORD_X ] [-COORD_Y ] [-INTERCEPT ] [-METHOD ] [-P_VALUE ] [-CROSSVAL ] [-CROSSVAL_K ] [-RESIDUAL_COR ]
  -PREDICTORS:  	Predictors
	grid list, input
  -POINTS:      	Points
	shapes, input
  -ATTRIBUTE:   	Dependent Variable
	table field
  -INFO_COEFF:  	Details: Coefficients
	table, output, optional
  -INFO_MODEL:  	Details: Model
	table, output, optional
  -INFO_STEPS:  	Details: Steps
	table, output, optional
  -RESIDUALS:   	Residuals
	shapes, output, optional
  -REGRESSION:  	Regression
	grid, output
  -REGRESCORR:  	Regression with Residual Correction
	grid, output, optional
  -RESAMPLING:  	Resampling
	choice
	Available Choices:
	[0] Nearest Neighbour
	[1] Bilinear Interpolation
	[2] Bicubic Spline Interpolation
	[3] B-Spline Interpolation
	Default: 3
  -COORD_X:     	Include X Coordinate
	boolean
	Default: 0
  -COORD_Y:     	Include Y Coordinate
	boolean
	Default: 0
  -INTERCEPT:   	Intercept
	boolean
	Default: 1
  -METHOD:      	Method
	choice
	Available Choices:
	[0] include all
	[1] forward
	[2] backward
	[3] stepwise
	Default: 3
  -P_VALUE:  	Significance Level
	floating point number
	Minimum: 0.000000
	Maximum: 100.000000
	Default: 5.000000
  -CROSSVAL:    	Cross Validation
	choice
	Available Choices:
	[0] none
	[1] leave one out
	[2] 2-fold
	[3] k-fold
	Default: 0
  -CROSSVAL_K:  	Cross Validation Subsamples
	integer number
	Minimum: 2
	Default: 10
  -RESIDUAL_COR:	Residual Interpolation
	choice
	Available Choices:
	[0] Multilevel B-Spline Interpolation
	[1] Inverse Distance Weighted
	Default: 0