r.kappa(1grass)

🎀 r.kappa 🎀

okay bestie, welcome to r.kappa(1grass) — this is your GRASS User's Manual moment and honestly? iconic. Let's get into it.

🌸 NAME 🌸

r.kappa — Calculates error matrix and kappa parameter for accuracy assessment of classification result. literally what it says on the tin, no cap 💅

🔑 KEYWORDS 🔑

raster, statistics, classification

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✨ SYNOPSIS ✨

r.kappa
r.kappa --help
r.kappa [-whm] classification=name reference=name  [output=name]
[title=string]  format=name  [--overwrite]  [--help]  [--verbose]
[--quiet]  [--ui]

🚩 Flags 🚩

• -w — Wide report 🫶 132 columns (default: 80). she's wide, she's comfy.
• -h — No header in the report. skip the intro, straight to the numbers.
• -m — Print Matrix only. just the matrix. nothing else. periodt.
• --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 🧚

• classification=name [required] — Name of raster map containing classification result
• reference=name [required] — Name of raster map containing reference classes
• output=name — Name for output file containing error matrix and kappa. If not given write to standard output (so she just yaps to your terminal, love that 🙈)
• title=string — Title for error matrix and kappa. Default: ACCURACY ASSESSMENT
• format=name [required] — Output format. Options: plain, json. Default: plain
  • plain: Plain text output
  • json: JSON (JavaScript Object Notation)

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💖 DESCRIPTION 💖

So r.kappa tabulates the error matrix of classification result by crossing classified map layer with respect to reference map layer. Both overall kappa (accompanied by its variance) and conditional kappa values are calculated. And yes babes, this analysis program respects the current geographic region and mask settings — she's polite like that 🫶

r.kappa calculates the error matrix of the two map layers and prepares the table from which the report is to be created. kappa values for overall and each classes are computed along with their variances. Also percent of commission and omission error, total correct classified result by pixel counts, total area in pixel counts and percentage of overall correctly classified pixels are tabulated. that's a LOT and she does all of it, no sweat ✨

The report will be written to an output file which is in plain text format and named by user at prompt of running the program. To obtain machine readable version, specify a json output format. bestie the json is for when your robot friends need the data too 🤖💜

The body of the report is arranged in panels. The classified result map layer categories is arranged along the vertical axis of the table, while the reference map layer categories along the horizontal axis. Each panel has a maximum of 5 categories (9 if wide format) across the top. In addition, the last column of the last panel reflects a cross total of each column for each row. All of the categories of the map layer arranged along the vertical axis, i.e., the reference map layer, are included in each panel. There is a total at the bottom of each column representing the sum of all the rows in that column. it's giving spreadsheet but make it fashion 📊

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🌈 OUTPUT VARIABLES 🌈

All output variables (except kappa variance) have been validated to produce correct values in accordance to formulas given by Rossiter, D.G., 2004. "Technical Note: Statistical methods for accuracy assessment of classified thematic maps".

ngl the kappa variance is out here unvalidated. she's the wild child of the group. correct with caution bestie 😭

• Observations — Overall count of observed cells (sum of both correct and incorrect ones).
• Correct — Overall count of correct cells (cells with equal value in reference and classification maps).
• Overall accuracy — Number of correct cells divided by overall cell count (expressed in percent).
• User's accuracy — Share of correctly classified cells out of all cells classified as belonging to specified class (expressed in percent). Inverse of commission error.
• Commission — Commission error = 100 - user's accuracy.
• Producer's accuracy — Share of correctly classified cells out of all cells known to belong to specified class (expressed in percent). Inverse of omission error.
• Omission — Omission error = 100 - producer's accuracy.
• Kappa — Choen's kappa index value. (yes that's how it's spelled here, we don't edit the legend 💅)
• Kappa variance — Variance of kappa index. Correctness needs to be validated.
• Conditional kappa — Conditional user's kappa for specified class.
• MCC — Matthews (Mattheus) Correlation Coefficient is implemented according to Grandini, M., Bagli, E., Visani, G. 2020. "Metrics for multi-class classification: An overview."

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📝 NOTES 📝

It is recommended to reclassify categories of classified result map layer into a more manageable number before running r.kappa on the classified raster map layer. Because r.kappa calculates and then reports information for each and every category. fr fr — she will report every single one, bestie, don't test her 🙈

NA's in output mean it was not possible to calculate the value (e.g. calculation would involve division by zero). In JSON output NA's are represented with value null. If there is no overlap between both maps, a warning is printed and output values are set to 0 or null respectively.

The Estimated kappa value in r.kappa is the value only for one class, i.e. the observed agreement between the classifications for those observations that have been classified by classifier 1 into the class i. In other words, here the choice of reference is important.

It is calculated as:

kpp[i] = (pii[i] - pi[i] * pj[i]) / (pi[i] - pi[i] * pj[i]);

where=

• pii[i] is the probability of agreement (i.e. number of pixels for which there is agreement divided by total number of assessed pixels)
• Pi[i] is the probability of classification i having classified the point as i
• Pj[i] is the probability of classification j having classified the point as i.

Some of reported values (overall accuracy, Choen's kappa, MCC) can be misleading if cell count among classes is not balanced. See e.g. Powers, D.M.W., 2012. "The Problem with Kappa"; Zhu, Q., 2020. "On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset".

🚨 heads up queen: imbalanced classes can make these numbers lie to you. lowkey read those papers, it's giving self-care 📚

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🍓 EXAMPLE 🍓

Example for North Carolina sample dataset:

g.region raster=landclass96 -p
r.kappa -w classification=landuse96_28m reference=landclass96
# export Kappa matrix as CSV file "kappa.csv"
r.kappa classification=landuse96_28m reference=landclass96 output=kappa.csv -m -h

Verification of classified LANDSAT scene against training areas:

r.kappa -w classification=lsat7_2002_classes reference=training

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🦋 SEE ALSO 🦋

g.region, r.category, r.mask, r.reclass, r.report, r.stats

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👑 AUTHORS 👑

• Tao Wen, University of Illinois at Urbana-Champaign, Illinois
• Maris Nartiss, University of Latvia (JSON output, MCC)

⭐ SOURCE CODE ⭐

Available at: r.kappa source code (history)

Latest change: Wednesday Feb 04 22:04:13 2026 in commit: 0aa5df790136477dd3209409135afabda18a67a7

Main index | Raster index | Topics index | Keywords index | Graphical index | Full index

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okay that's the whole page, you ate that bestie 💅✨ this was GRASS 8.5.0, r.kappa(1grass), © 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual, last touched Wednesday Feb 04 22:04:13 2026 — go run it and slay 🫶🌸