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r.covar

Outputs a covariance/correlation matrix for user-specified raster map layer(s).

r.covar [-r] map=name [,name,...] [--verbose] [--quiet] [--qq] [--ui]

Example:

r.covar map=name

grass.script.run_command("r.covar", map, flags=None, verbose=False, quiet=False, superquiet=False)

Example:

gs.run_command("r.covar", map="name")

Parameters

map=name [,name,...] [required]
    Name of raster map(s)
-r
    Print correlation matrix
--help
    Print usage summary
--verbose
    Verbose module output
--quiet
    Quiet module output
--qq
    Very quiet module output
--ui
    Force launching GUI dialog

map : str | list[str], required
    Name of raster map(s)
    Used as: input, raster, name
flags : str, optional
    Allowed values: r
    r
        Print correlation matrix
verbose: bool, optional
    Verbose module output
    Default: False
quiet: bool, optional
    Quiet module output
    Default: False
superquiet: bool, optional
    Very quiet module output
    Default: False

DESCRIPTION

r.covar outputs a covariance/correlation matrix for user-specified raster map layer(s). The output can be printed, or saved by redirecting output into a file.

The output is an N x N symmetric covariance (correlation) matrix, where N is the number of raster map layers specified on the command line.

NOTES

This module can be used as the first step of a principle components transformation. The covariance matrix would be input into a system which determines eigen values and eigen vectors. An NxN covariance matrix would result in N real eigen values and N eigen vectors (each composed of N real numbers).

The module m.eigensystem in GRASS GIS Addons can be installed and used to generate the eigenvalues and vectors.

EXAMPLE

For example,

g.region raster=layer.1 -p
r.covar -r map=layer.1,layer.2,layer.3

would produce a 3x3 matrix (values are example only):

     1.000000  0.914922  0.889581
     0.914922  1.000000  0.939452
     0.889581  0.939452  1.000000

In the above example, the eigen values and corresponding eigen vectors for the covariance matrix are:

component   eigen value               eigen vector
    1       1159.745202   <0.691002  0.720528  0.480511>
    2          5.970541   <0.711939 -0.635820 -0.070394>
    3        146.503197   <0.226584  0.347470 -0.846873>

The component corresponding to each vector can be produced using r.mapcalc as follows:

r.mapcalc "pc.1 = 0.691002*layer.1 + 0.720528*layer.2 + 0.480511*layer.3"
r.mapcalc "pc.2 = 0.711939*layer.1 - 0.635820*layer.2 - 0.070394*layer.3"
r.mapcalc "pc.3 = 0.226584*layer.1 + 0.347470*layer.2 - 0.846873*layer.3"

Note that based on the relative sizes of the eigen values, pc.1 will contain about 88% of the variance in the data set, pc.2 will contain about 1% of the variance in the data set, and pc.3 will contain about 11% of the variance in the data set. Also, note that the range of values produced in pc.1, pc.2, and pc.3 will not (in general) be the same as those for layer.1, layer.2, and layer.3. It may be necessary to rescale pc.1, pc.2 and pc.3 to the desired range (e.g. 0-255). This can be done with r.rescale.

SEE ALSO

i.pca, m.eigensystem (Addon), r.mapcalc, r.rescale

AUTHOR

Michael Shapiro, U.S. Army Construction Engineering Research Laboratory

SOURCE CODE

Available at: r.covar source code (history)
Latest change: Monday Mar 17 14:32:15 2025 in commit c35745f