GRASS GIS manual: v.normal

Note: A new GRASS GIS stable version has been released: GRASS GIS 7.6, available here.
Updated manual page: here

NAME

v.normal - Tests for normality for vector points.

KEYWORDS

vector, statistics, points, point pattern

SYNOPSIS

v.normal

v.normal --help

v.normal [-rl] map=name [layer=string] tests=range[,range,...] column=name [--help] [--verbose] [--quiet] [--ui]

Flags:

-r: Use only points in current region
-l: Lognormality instead of normality
--help: Print usage summary
--verbose: Verbose module output
--quiet: Quiet module output
--ui: Force launching GUI dialog

Parameters:

map=name [required]: Name of vector map; Or data source for direct OGR access
layer=string: Layer number or name; Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.; Default: 1
tests=range[,range,...] [required]: Lists of tests (1-15); E.g. 1,3-8,13
column=name [required]: Name of attribute column

DESCRIPTION
NOTES
EXAMPLE
SEE ALSO
AUTHOR

DESCRIPTION

v.normal computes tests of normality on vector points.

NOTES

The tests that v.normal performs are indexed below. The tests that are performed are specified by giving an index, ranges of indices, or multiple thereof.

Sample skewness and kurtosis
Geary's a-statistic and an approximate normal transformation
Extreme normal deviates
D'Agostino's D-statistic
Modified Kuiper V-statistic
Modified Watson U^2-statistic
Durbin's Exact Test (modified Kolmogorov)
Modified Anderson-Darling statistic
Modified Cramer-Von Mises W^2-statistic
Kolmogorov-Smirnov D-statistic (modified for normality testing)
Chi-Square test statistic (equal probability classes) and the number of degrees of freedom
Shapiro-Wilk W Test
Weisberg-Binghams W'' (similar to Shapiro-Francia's W')
Royston's extension of W for large samples
Kotz Separate-Families Test for Lognormality vs. Normality

EXAMPLE

Compute the sample skewness and kurtosis, Geary's a-statistic and an approximate normal transformation, extreme normal deviates, and Royston's W for the random vector points:

g.region raster=elevation -p
v.random random n=200
v.db.addtable random column="elev double precision"
v.what.rast random rast=elevation column=elev
v.normal random tests=1-3,14 column=elev