r.regression.linegrass - Online in the Cloud

PROGRAM:

NAME

r.regression.line - 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

Use r.regression.linegrass online using onworks.net services