[R] p-values

[Peter Ho]

Spencer,


Thank you for referring me to your other email on Exact goodness-of-fit 
test. However, I'm not entirely sure if what you mentioned is the same 
for my case. I'm not a statistician and it would help me if you could 
explain what you meant in a little more detail. Perhaps I need to 
explain the problem in more detail.

I am looking for a way to calculate exaxt p-values by Monte Carlo 
Simulation for Durbin's test. Durbin's test statistic is similar to 
Friedman's statistic, but considers the case of Balanced Incomplete 
block designs. I have found a function written by Felipe de Mendiburu 
for calculating Durbin's statistic, which gives the chi-squared p-value. 
I have also been read an article by Torsten Hothorn "On exact rank Tests 
in R" (R News 1(1), 11–12.) and he has shown how to calculate Monte 
Carlo p-values using pperm. In the article by Torsten Hothorn he gives:

R> pperm(W, ranks, length(x))

He compares his method to that of StatXact, which is the program Rayner 
and Best suggested using. Is there a way to do this for example for the 
friedman test.

A paper by Joachim Rohmel discusses "The permutation distribution for 
the friendman test" (Computational Statistics & Data Analysis 1997, 26: 
83-99). This seems to be on the lines of what I need, although I am not 
quite sure. Has anyone tried to recode his APL program for R?

I have tried a number of things, all unsucessful. Searching through 
previous postings have not been very successful either. It seems that 
pperm is the way to go, but I would need help from someone on this.

Any hints on how to continue would be much appreciated.


Peter


Spencer Graves wrote:

>Hi, Peter:
>
>	  Please see my reply of a few minutes ago subject:  exact 
>goodness-of-fit test.  I don't know Rayner and Best, but the same 
>method, I think, should apply.  spencer graves
>
>Peter Ho wrote:
>
>  
>
>>HI R-users,
>>
>>I am trying to repeat an example from Rayner and Best "A contingency 
>>table approach to nonparametric testing (Chapter 7, Ice cream example).
>>
>>In their book they calculate Durbin's statistic, D1, a dispersion 
>>statistics, D2, and a residual. P-values for each statistic is 
>>calculated from a chi-square distribution and also Monte Carlo p-values.
>>
>>I have found similar p-values based on the chi-square distribution by 
>>using:
>>
>> > pchisq(12, df= 6, lower.tail=F)
>>[1] 0.0619688
>> > pchisq(5.1, df= 6, lower.tail=F)
>>[1] 0.5310529
>>
>>Is there a way to calculate the equivalent Monte Carlo p-values?
>>
>>The values were 0.02 and 0.138 respectively.
>>
>>The use of the approximate chi-square probabilities for Durbin's test 
>>are considered not good enough according to Van der Laan (The American 
>>Statistician 1988,42,165-166).
>>
>>
>>Peter
>>--------------------------------
>>ESTG-IPVC
>>
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>>    
>>
>
>  
>