(I'm replying to your original post because your follow-up omits the
context.)

The K-S test is designed for continuous distributions. You have far
too many zeros in your data to get anything reasonable out of the
test. For your data, the K-S statistic is the difference in the
(e)cdfs at zero. Your results just show that this can be sensitive
to the degree of rounding used for the theoretical cdf.

Peter Ehlers

On 2011-07-29 02:07, Jochen1980 wrote:
Hi,

I got two data point vectors. Now I want to make a ks.test(). I you print
both vectors you will see, that they fit pretty fine. Here is a picture:
http://www.jochen-bauer.net/downloads/kstest-r-help-list-plot.png

As you can see there is one histogram and moreover there is the gumbel
density
function plotted. Now I took to bin-mids and the bin-height for vector1 and
computed the distribution-values to all bin-mids as vector2.

I pass these two vectors to ks.test(). Are those the right vectors, if I
want
to decide afterwards, if my experiment-data is gumbel-distributed?

Surprisingly the p-value changes tremendously if I calculate more digits out
of
my theoretical formula. If I round to 0 digits, p is 1, if I round to 4
digits,
p drops to 0 - how could this happen, I thought more digits will bring more
accurate results?!

XXXX Case 0 digits: XXXXXXXXXXXXXXXXXXXXXXXXXXX
   [1]   0   0   0   0   0  24  74  98 133 147 134 120  89  69  46  31  16
7
  [19]   7   3   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [37]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [55]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [73]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [91]   0   0   0   0   0   0   0   0   0   0
   [1]   0   0   0   0   1  10  49 113 160 168 147 113  81  55  37  24  15
10
  [19]   6   4   2   2   1   1   0   0   0   0   0   0   0   0   0   0   0
0
  [37]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [55]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [73]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [91]   0   0   0   0   0   0   0   0   0   0
[1] "Ergebnisse"
[1] "Analyse der Eingangsdaten"
[1] "Mean:  0.104537195"
[1] "SAbw.:  0.0277657985898433"
[1] "Parameter-Berechnung der Daten bei angenommener Gumbelverteilung"
[1] "Mue:  0.0920411082987717"
[1] "Beta:  0.0216489043196013"
[1] "KS-Test ->   1000  Werte,  100  Bins, x: Klassenmitten, y1, y2 =
Histogrammhöhen"
[1] "KST D:  0.04"
[1] "KST P:  1"

XXX Case 4 digits: XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
   [1]   0   0   0   0   0  24  74  98 133 147 134 120  89  69  46  31  16
7
  [19]   7   3   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [37]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [55]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [73]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
0
  [91]   0   0   0   0   0   0   0   0   0   0
   [1]   0.000   0.000   0.000   0.006   0.622  10.094  49.271 112.776
160.174
  [10] 168.419 146.527 113.137  81.026  55.344  36.690  23.870  15.347
9.793
  [19]   6.220   3.939   2.490   1.572   0.992   0.625   0.394   0.248
0.157
  [28]   0.099   0.062   0.039   0.025   0.016   0.010   0.006   0.004
0.002
  [37]   0.002   0.001   0.001   0.000   0.000   0.000   0.000   0.000
0.000
  [46]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
  [55]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
  [64]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
  [73]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
  [82]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
  [91]   0.000   0.000   0.000   0.000   0.000   0.000   0.000   0.000
0.000
[100]   0.000
[1] "Ergebnisse"
[1] "Analyse der Eingangsdaten"
[1] "Mean:  0.104537195"
[1] "SAbw.:  0.0277657985898433"
[1] "Parameter-Berechnung der Daten bei angenommener Gumbelverteilung"
[1] "Mue:  0.0920411082987717"
[1] "Beta:  0.0216489043196013"
[1] "KS-Test ->   1000  Werte,  100  Bins, x: Klassenmitten, y1, y2 =
Histogrammhöhen"
[1] "KST D:  0.2"
[1] "KST P:  0.0366"

Thanks in advance for some help.
Jochen

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