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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