r.regression.multi(1)

NAME
r.regression.multi - Calculates multiple linear regression from raster maps.

KEYWORDS
raster, statistics, regression

SYNOPSIS
r.regression.multi
r.regression.multi --help
r.regression.multi [-g] mapx=name[,name,...] mapy=name [residuals=name] [estimates=name] [output=name] format=name [--overwrite] [--help] [--verbose] [--quiet] [--ui]

  Flags:

-g
    Print in shell script style [deprecated]
    This flag is deprecated and will be removed in a future release. Use format=shell instead.

--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[,name,...] [required]
    Map for x coefficient

mapy=name [required]
    Map for y coefficient

residuals=name
    Map to store residuals

estimates=name
    Map to store estimates

output=name
    ASCII file for storing regression coefficients (output to screen if file not specified).

format=name [required]
    Output format
    Options: shell, json
    Default: shell
    shell: shell script style text output
    json: JSON (JavaScript Object Notation)

DESCRIPTION
r.regression.multi calculates a multiple linear regression from raster maps, according to the formula

Y = b0 + sum(bi*Xi) + E

where

X = {X1, X2, ..., Xm}
m = number of explaining variables
Y = {y1, y2, ..., yn}
Xi = {xi1, xi2, ..., xin}
E = {e1, e2, ..., en}
n = number of observations (cases)

In R notation:

Y ~ sum(bi*Xi)
b0 is the intercept, X0 is set to 1

r.regression.multi is designed for large datasets that can not be processed in R. A p value is therefore not provided, because even very small, meaningless effects will become significant with a large number of cells. Instead it is recommended to judge by the estimator b, the amount of variance explained (R squared for a given variable) and the gain in AIC (AIC without a given variable minus AIC global must be positive) whether the inclusion of a given explaining variable in the model is justified.

  The global model
The b coefficients (b0 is offset), R squared or coefficient of determination (Rsq) and F are identical to the ones obtained from R-stats’s lm() function and R-stats’s anova() function. The AIC value is identical to the one obtained from R-stats’s stepAIC() function (in case of backwards stepping, identical to the Start value). The AIC value corrected for the number of explaining variables and the BIC (Bayesian Information Criterion) value follow the logic of AIC.

  The explaining variables
R squared for each explaining variable represents the additional amount of explained variance when including this variable compared to when excluding this variable, that is, this amount of variance is explained by the current explaining variable after taking into consideration all the other explaining variables.

The F score for each explaining variable allows testing if the inclusion of this variable significantly increases the explaining power of the model, relative to the global model excluding this explaining variable. That means that the F value for a given explaining variable is only identical to the F value of the R-function summary.aov if the given explaining variable is the last variable in the R-formula. While R successively includes one variable after another in the order specified by the formula and at each step calculates the F value expressing the gain by including the current variable in addition to the previous variables, r.regression.multi calculates the F-value expressing the gain by including the current variable in addition to all other variables, not only the previous variables.

The AIC value is identical to the one obtained from the R-function stepAIC() when excluding this variable from the full model. The AIC value corrected for the number of explaining variables and the BIC value (Bayesian Information Criterion) value follow the logic of AIC. BIC is identical to the R-function stepAIC with k = log(n). AICc is not available through the R-function stepAIC.

EXAMPLE
Multiple regression with soil K-factor and elevation, aspect, and slope (North Carolina dataset). Output maps are the residuals and estimates:

g.region raster=soils_Kfactor -p
r.regression.multi mapx=elevation,aspect,slope mapy=soils_Kfactor \
  residuals=soils_Kfactor.resid estimates=soils_Kfactor.estim

SEE ALSO
d.correlate, r.regression.line, r.stats

AUTHOR
Markus Metz

SOURCE CODE

Available at: r.regression.multi source code (history)

Latest change: Saturday Oct 11 04:38:40 2025 in commit: 3dd71f5fab83b72a2db2da959fe075c5b3b52200

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