r.regression.line.1grass - Man Page
Calculates linear regression from two raster maps: y = a + b*x.
Keywords
raster, statistics, regression
Synopsis
r.regression.line
r.regression.line --help
r.regression.line [-g] mapx=name mapy=name [output=name] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags
- -g
Print in shell script style
- --overwrite
Allow output files to overwrite existing files
- --help
Print usage summary
- --verbose
Verbose module output
- --quiet
Quiet module output
- --ui
Force launching GUI dialog
Parameters
- mapx=name [required]
Map for x coefficient
- mapy=name [required]
Map for y coefficient
- output=name
ASCII file for storing regression coefficients (output to screen if file not specified).
Description
r.regression.line calculates a linear regression from two raster maps, according to the formula
y = a + b*x
where
x y
represent the input raster maps.
Optionally, it saves regression coefficients as a ASCII file. The result includes the following coefficients: offset/intercept (a) and gain/slope (b), correlation coefficient (R), number of elements (N), means (medX, medY), standard deviations (sdX, sdY), and the F test for testing the significance of the regression model as a whole (F).
Notes
The results for offset/intercept (a) and gain/slope (b) are identical to that obtained from R-stats’s lm() function.
Example
Comparison of two DEMs (SRTM and NED, both at 30m resolution), provided in the North Carolina sample dataset:
g.region raster=elev_srtm_30m -p r.regression.line mapx=elev_ned_30m mapy=elev_srtm_30m y = a + b*x a (Offset): -1.659279 b (Gain): 1.043968 R (sumXY - sumX*sumY/N): 0.894038 N (Number of elements): 225000 F (F-test significance): 896093.366283 meanX (Mean of map1): 110.307571 sdX (Standard deviation of map1): 20.311998 meanY (Mean of map2): 113.498292 sdY (Standard deviation of map2): 23.718307
Using the script style flag AND eval to make results available in the shell:
g.region raster=elev_srtm_30m -p eval `r.regression.line -g mapx=elev_ned_30m mapy=elev_srtm_30m` # print result stored in respective variables echo $a -1.659279 echo $b 1.043968 echo $R 0.894038
See Also
d.correlate, r.regression.multi, r.stats
Authors
Dr. Agustin Lobo - alobo at ija.csic.es
Updated to GRASS 5.7 Michael Barton, Arizona State University
Script style output Markus Neteler
Conversion to C module Markus Metz
Source Code
Available at: r.regression.line source code (history)
Accessed: Monday Sep 09 08:47:59 2024
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