This is one way. I read your two columns of data into
two variables, x and y. Then:
test <- replicate(5000, cor(x, sample(y)))> hist(test)
Kjetil
On Thu, Oct 28, 2010 at 9:49 AM, Chitra <cbbaniya at gmail.com>
wrote:>
>> Dear R experts,
>>I am sorry for my inability.
>>I have the following dataset:
>
> ?> Qtot Itot
>>1 ? 73 ?684
>>2 ? 64 ?451
>>3 ? 71 ?378
>>4 ? 65 ?284
>>5 ? 47 ?179
>>6 ? 31 ?117
>>7 ? 19 ? 69
>>
>>Now I need to perform Monte Carlo Pertutation test underlaying the
> following condition.
>>
>>
>>Condition
>>
>>In order to choose randomly (5000 times) for the Qtot in the interval 0
to
> the maximum possible value of
>>
>>Qtot for each observed value of the independent variable (Itot), Monte
> Carlo tests needed to be perform to test the null hypothesis. The
>>maximum observed value is either the calculated size of the regional
pool
> (Interpolated value), or the measured size of the actual
>>
>>pool. Each time correlation coefficient (r) between independent and
> dependent variable has to be calculated in order to achieve
>>
>>the empirical distribution of r for the null hypothesis conditions.
>>
>>Now please help me how to do?
>>I have also intalled combinat library in R. But could not perform by
> myself.
>>I will be highly grateful.
>>Thanks.
>>
> -->
> Chitra Bahadur Baniya (PhD)
> Lecturer
> Central Department of Botany
> Tribhuvan University
> Kirtipur
> Kathmandu, Nepal
>
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