I have submitted some comments upon the documentation of the "r.regression.line" script in http://wald.intevation.org/tracker/?func=detail&aid=552&group_id=21&atid=207
Now I have 2 questions. Apologies if I miss some obvious things but I am confused! Here it goes: 1. I don't understand why (in lines 84 and 85 in the r.regression.line script) "sumsqX=sumsqX/tot" and "sumsqY=sumsqY/tot" ? 2. I can't understand the differences in the following... : I created two raster maps with the same MASK, each containing only 6 pixels with the following values: mapA: 326 641 1336 2020 3197 3484 mapB: 432 850 931 1956 2582 2622 For mapA "r.univar" gives: n: 6 minimum: 326 maximum: 3484 range: 3158 mean: 1834 mean of absolute values: 1834 standard deviation: 1194.44 variance: 1.4267e+06 variation coefficient: 65.1278 % sum: 11004) For mapB... : n: 6 minimum: 432 maximum: 2622 range: 2190 mean: 1562.17 mean of absolute values: 1562.17 standard deviation: 866.145 variance: 750207 variation coefficient: 55.4451 % sum: 9373) In openoffice calc (some of) the respective results are: For mapA: MIN: 326 MAX: 3484 AVERAGE: 1834 STDEV: 1308,45 VAR: 1,71E+06 SUM: 11004 For mapB: MIN: 432 MAX: 2622 AVERAGE: 156217 STDEV: 948,81 VAR: 9,00E+05 SUM: 9373 Based on r.regression.line I get for map1=mapA and map2=mapB: a b R N F medX sdX medY sdY 0.000458151 0.809242 0.99157 1021726 -0.98321 0.01077 5.3038 0.00917369 4.32854 and for map1=mapB and map2=mapB: a b R N F medX sdX medY sdY -0.000375823 1.21498 0.99157 1021726 -0.98321 0.00917369 4.32854 0.01077 5.3038 "R" is Pearson's correlation coefficient (as correctly defined in the script "r.regression.line" in line 83 but wrongly expressed as "sumXY - sumX*sumY/tot" in the print-out in line 101). In openoffice-calc I get for these: MapA MapB 326 432 1,350792 Slope m 641 850 0,138835 standard error of the slope 1336 931 0,959458 RSQ (Square of "r") 2020 1956 94,662802 4,000000 F value from the variance analysis std error of regression for Y 3197 2582 8213134,001379 sum of squared deviation of estimated Y values from their linear mean 3484 2622 or MapB MapB 432 326 0,710293 Slope m 850 641 0,073004 standard error of the slope 931 1336 0,959458 RSQ 1956 2020 94,662802 4,000000 F value from the variance analysis std error of regression for Y 2582 3197 4318750,949062 sum of squared deviation of estimated Y values from their linear mean 2622 3484 (How is really r.regression.line functioning? Trying to interpret the script is not that easy for me since I lack of some basics in scripting) Thank you, Nikos. _______________________________________________ grass-user mailing list grass-user@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-user
