Hi,
I have two sets of data, an observed data and generated data.
The generated data is obtained from the model where the parameters is estimated
from the observed data.
So I'm not sure which to use either
one-sample test
ks.test(x+2, "pgamma", 3, 2) # two-sided, exact
or
two-sample test
ks.test(x, x2, alternative="l")
If I use the one-sample test I need to specified the model which I don't have
in
my case.
Actually I use the two-sample test and when I compare with what I got from
using
Chi-square test the result is too different.
Data:
obs_data pre_gam
[1,] 93 25.6770
[2,] 115 127.9095
[3,] 125 151.6845
[4,] 120 146.9295
[5,] 106 107.9385
[6,] 101 107.4630
[7,] 75 86.5410
[8,] 58 55.6335
[9,] 46 43.7460
[10,] 38 32.8095
[11,] 31 16.1670
[12,] 17 18.5445
[13,] 10 9.0345
[14,] 16 20.9220
Results:
> chisq.test(obs_data, p = pre_gam, rescale.p = TRUE)
Chi-squared test for given probabilities
data: obs_data
X-squared = 205.4477, df = 13, p-value < 2.2e-16
> ks.test(obs_data,pre_gam)
Two-sample Kolmogorov-Smirnov test
data: obs_data and pre_gam
D = 0.2143, p-value = 0.9205
alternative hypothesis: two-sided
Am I doing the right thing?
Thank you so much for your help.
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