Hi All,
I have some data about the survey of asthma patients and some of the asthma causing agents in the study area. can you please suggest me the best statistical methods to analyse and represent the data in a best way. I am an amateur in the biostatistics. Kindly help me out.
Thanking you with regards
Yours sincerely 

Dr. A. H. Manjunatha Reddy
Lecturer, 
Department of Biotechnology
R V College of Engineering
Mysore Rd
Bangalore – 560 059
INDIA
Ph: +91 98445 73697

--- On Wed, 13/5/09, Erika Mudrak <mudrak@wisc.edu> wrote:


From: Erika Mudrak <mudrak@wisc.edu>
Subject: Re: [R-sig-eco] testing for distribution
To: "Capelle, Jacob" <Jacob.Capelle@wur.nl>
Cc: r-sig-ecology@r-project.org
Date: Wednesday, 13 May, 2009, 11:47 PM


Jacob-  You can use a Chi-squared goodness of fit - chisq.test() for discrete distributions like the negative binomial and a Kolmogorov-Smirnoff test- ks.test() for continuous distributions.      They will both produce a p-value which tests the null hypothesis that your data come from the given distribution with stated parameters.    Use the parameter estimates from your fitdistr() results. So if p>0.05 (or 0.1 or whatever), your data come from that distribution. 

For Discrete distributions, try something like: 
fit=fitdistr(.....)
chisq.test(x=ActualData, y=rnbinom(n=length(ActualData), k=fit.k, mu=fit.mu))
#I think this is right, I haven't actually tried it...
# This is akin to quantitatively comparing your histograms...


For continous distributions (such as beta), the code would be this: 
fit=fitdistr(...)
ks.test(ActualData, "pbeta", shape1=fit$estimate[1],shape2=fit$estimate[2])
# I've done this successfully

You can use AIC to test if another distribution fits your data better than negative binomial does.  I think it's possible for your data to "pass" the Chi-Squared/Kolmogorov-Smirnoff test for two different distributions, but it will fit one better than another. 

Erika Mudrak


-------------------------------------------
Erika Mudrak
Graduate Student
Department of Botany
University of Wisconsin-Madison
430 Lincoln Dr
Madison WI, 53706
608-265-2191
mudrak@wisc.edu

----- Original Message -----
From: "Capelle, Jacob" <Jacob.Capelle@wur.nl>
Date: Tuesday, May 12, 2009 11:00 am
Subject: [R-sig-eco]  testing for distribution
To: r-sig-ecology@r-project.org


> Dear all,
>  
> I have a kind of a theoretical question from which I hope it might 
> interest you and hopefully can help me a bit.
>  
> In order to obtain ecological (surrvey) data, I try to make a 
> prediction about the accuracy of a sampling tool to estimate mussel 
> density. For this reason I took a lot of samples at a certain fixed 
> location and counted the amount of mussels in each sample. Because 
> mussels are aggregated on the sediment, I had a lot of zero values. To 
> estimate the sample size I used a binomial distribution and obtained 
> the k value and the mu from the fitdistr(x,"negative binomial") (MASS).
>  
> The question I have is: how can I test if this distribution accurately 
> described my (zero inflated count) data?
>  
> I am a bit familiar with the AIC but since I only have counts on one 
> variable I cannot perform a GLS. 
> Creating a vector with rnbinom() using the k and mu from the 
> fitdistr() I plotted a histogram and compared it with my data, this 
> showed that is was roughly comparable, but I want to quantify this.
>  
> I have a biological background not a statistical one, so I realize I 
> can ask silly questions.
> But I hope someone can give me some hints. 
>  
> Kind regards,
>  
> Jacob Capelle
>  
> PhD student
> Wageningen Imares
> The Netherlands
> jacob.capelle@wur.nl < 
> 
> _______________________________________________
> R-sig-ecology mailing list
> R-sig-ecology@r-project.org
> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology

_______________________________________________
R-sig-ecology mailing list
R-sig-ecology@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-ecology



      New Email names for you! 

il. 
[[elided Yahoo spam]]

	[[alternative HTML version deleted]]

