[R] R: chi-Squared distribution

2005-01-21 Thread Vito Ricci
Hi,
Attention chi-squared distribution, unlike F
distribution, has only df1 as parameter, not df1 and
df2. So correct into:

outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1,
df2))

outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1))
 

Regards,
Vito


you wrote:

Dear Rs:


outer(1:3, 1:3, function(df1, df2) qf(0.95, df1, df2))
 

I compare this F distribution results with the table,
the answers were perfect. But I need to see for
chi-sqaured distribution. When I employed the similar
formula

outer(1:3, 1:3, function(df1, df2) qchisq(0.95, df1,
df2)) , I am getting unexpected results. I need to see
the following values:

 p=0.750  .
1 1.323

2 2.773   

3 4.108   


Thanking you
Prasanna

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[R] R: chi-Squared distribution in Friedman test

2005-01-21 Thread Vito Ricci
Hi,

pchisq - distribution function
dchisq - density function

pval is the area under the curve, to calculte it you
use distribution function which is the integral of
density function. See:

http://www.itl.nist.gov/div898/handbook/eda/section3/eda362.htm
http://mathworld.wolfram.com/DistributionFunction.html


f(x) density function
F(x) distribution function =Pr(Xx)= integral(f(x))

Hoping I helped you!
Regards
Vito

you wrote:

Dear R helpers:

Thanks for the previous reply. I am using Friedman
racing test. According the the book Pratical
Nonprametric Statistic by WJ Conover, after computing
the statistics, he suggested to use chi-squared or F
distribution to accept or reject null hypothesis.
After looking into the source code, I found that R
uses chi-sqaured distribution as below:

PVAL - pchisq(STATISTIC, PARAMETER, lower = FALSE)

but still I cant figure out why they are using this
pschisq insted of dchisq. Sorry I am wrong!!


Thanking you
truly
Prasanna











=
Diventare costruttori di soluzioni
Became solutions' constructors

The business of the statistician is to catalyze 
the scientific learning process.  
George E. P. Box

Top 10 reasons to become a Statistician

 1. Deviation is considered normal
 2. We feel complete and sufficient
 3. We are 'mean' lovers
 4. Statisticians do it discretely and continuously
 5. We are right 95% of the time
 6. We can legally comment on someone's posterior distribution
 7. We may not be normal, but we are transformable
 8. We never have to say we are certain
 9. We are honestly significantly different
10. No one wants our jobs


Visitate il portale http://www.modugno.it/
e in particolare la sezione su Palese  http://www.modugno.it/archivio/palese/

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