Hi
I tirend used r.quantile on SRTM data for Poland (min value about to 0, max value about to 2500 m a.s.l)
r.quantile input=Polska@PERMANENT quantiles=4 percentiles=0.001,0.01,0,1,0.25,0.50,0.75,0.90,0.99,0.999 bins=1000000
and I recived:
0:0.000000:0.000000
1:0.001000:0.000000
2:0.010000:0.000000
3:0.250000:0.000000
4:0.500000:1.000000
5:0.750000:2.000000
6:0.900000:3.000000
7:0.990000:3.000000
8:0.999000:3.000000
9:1.000000:3.000000
It looks like the results are divided by 1000 and rounded to the nearest integer.
Did I something wrong?
Jarek
On Fri, Jan 2, 2009 at 4:55 AM, Jarek Jasiewicz <jarekj@amu.edu.pl> wrote:
Hi
I tirend used r.quantile on SRTM data for Poland (min value about to 0, max
value about to 2500 m a.s.l)
r.quantile input=Polska@PERMANENT quantiles=4
percentiles=0.001,0.01,0,1,0.25,0.50,0.75,0.90,0.99,0.999 bins=1000000
and I recived:
0:0.000000:0.000000
1:0.001000:0.000000
2:0.010000:0.000000
3:0.250000:0.000000
4:0.500000:1.000000
5:0.750000:2.000000
6:0.900000:3.000000
7:0.990000:3.000000
8:0.999000:3.000000
9:1.000000:3.000000
It looks like the results are divided by 1000 and rounded to the nearest
integer.
Did I something wrong?
Jarek
I thought that the user supplies one of [quantiles=] | [percentiles=]
... could that be related to the odd output. I have verified (with R)
that r.quantile can compute correct quantiles... however, on my system
I need to set bins=100 or so, as the higher i set 'bins' odd things
would happen.
Glynn should know for sure.
Dylan
Dylan Beaudette pisze:
On Fri, Jan 2, 2009 at 4:55 AM, Jarek Jasiewicz <jarekj@amu.edu.pl> wrote:
Hi
I tirend used r.quantile on SRTM data for Poland (min value about to 0, max
value about to 2500 m a.s.l)
r.quantile input=Polska@PERMANENT quantiles=4
percentiles=0.001,0.01,0,1,0.25,0.50,0.75,0.90,0.99,0.999 bins=1000000
and I recived:
0:0.000000:0.000000
1:0.001000:0.000000
2:0.010000:0.000000
3:0.250000:0.000000
4:0.500000:1.000000
5:0.750000:2.000000
6:0.900000:3.000000
7:0.990000:3.000000
8:0.999000:3.000000
9:1.000000:3.000000
It looks like the results are divided by 1000 and rounded to the nearest
integer.
Did I something wrong?
Jarek
I thought that the user supplies one of [quantiles=] | [percentiles=]
... could that be related to the odd output. I have verified (with R)
that r.quantile can compute correct quantiles... however, on my system
I need to set bins=100 or so, as the higher i set 'bins' odd things
would happen.
Glynn should know for sure.
Dylan
Thanks
it really seems that the problem was in default number of bins
On Fri, Jan 2, 2009 at 8:40 PM, Dylan Beaudette
<dylan.beaudette@gmail.com> wrote:
On Fri, Jan 2, 2009 at 4:55 AM, Jarek Jasiewicz <jarekj@amu.edu.pl> wrote:
Hi
I tirend used r.quantile on SRTM data for Poland (min value about to 0, max
value about to 2500 m a.s.l)
r.quantile input=Polska@PERMANENT quantiles=4
percentiles=0.001,0.01,0,1,0.25,0.50,0.75,0.90,0.99,0.999 bins=1000000
and I recived:
0:0.000000:0.000000
1:0.001000:0.000000
2:0.010000:0.000000
3:0.250000:0.000000
4:0.500000:1.000000
5:0.750000:2.000000
6:0.900000:3.000000
7:0.990000:3.000000
8:0.999000:3.000000
9:1.000000:3.000000
It looks like the results are divided by 1000 and rounded to the nearest
integer.
Did I something wrong?
Jarek
I thought that the user supplies one of [quantiles=] | [percentiles=]
... could that be related to the odd output. I have verified (with R)
that r.quantile can compute correct quantiles... however, on my system
I need to set bins=100 or so, as the higher i set 'bins' odd things
would happen.
Glynn should know for sure.
Can you please file a bug report on this? Preferable with a
Spearfish/NC reproducible example?
Thanks
Markus