hi Nikos,
I have replicated (partially) your experiment:
GRASS 6.3.svn (pat): > cat mapA.asc
north: 2
south: 0
east: 3
west: 0
rows: 2
cols: 3
326 641 1336
2020 3197 3484
GRASS 6.3.svn (pat): > cat mapB.asc
north: 2
south: 0
east: 3
west: 0
rows: 2
cols: 3
432 850 931
1956 2582 2622
r.in.ascii mapA.asc out=mapA
r.in.ascii mapB.asc out=mapB
g.region w=0 e=3 s=0 n=2 res=1 -p
r.regression.line map2=mapA map1=mapB -g
100%
a=-276.162
b=1.35079
R=0.979519
N=6
F=-0.827183
medX=1562.17
sdX=866.145
medY=1834
sdY=1194.44
# note the new -s flag for slow but accurate computation in
# case of FP maps
To compare, I run the same in R-stats:
library(spgrass6)
maps <- readRAST6(c("mapA","mapB"), cat=c(FALSE,FALSE))
names(maps) <- c("mapA","mapB")
mapA <- as.vector(maps$mapA)
mapB <- as.vector(maps$mapB)
lm(mapA ~ mapB)
Call:
lm(formula = mapA ~ mapB)
Coefficients:
(Intercept) mapB
-276.162 1.351
summary(lm(mapA ~ mapB))
Call:
lm(formula = mapA ~ mapB)
Residuals:
1 2 3 4 5 6
18.62 -231.01 354.57 -345.99 -14.58 218.39
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -276.1615 247.9893 -1.114 0.327852
mapB 1.3508 0.1388 9.729 0.000625 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 294.6 on 4 degrees of freedom
Multiple R-Squared: 0.9595, Adjusted R-squared: 0.9493
F-statistic: 94.66 on 1 and 4 DF, p-value: 0.0006249
It would be good to have a bit more statistics in r.regression.line.
Best,
Markus
Nikos Alexandris wrote:
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.
--
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