Try changing your method to "ML" and try again. I tried the run the
first example from the documentation and it failed with the same error.
Changing the estimation method to ML worked.
@List: Can anyone else verify the error I got? I literally ran the
following two lines interactively from the example for goodfit:
dummy <- rnbinom(200, size = 1.5, prob = 0.8)
gf <- goodfit(dummy, type = "nbinomial", method = "MinChisq")
and got back
Warning messages:
1: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced
2: In pnbinom(q, size, prob, lower.tail, log.p) : NaNs produced
Again, I hope this helps.
Sincerely,
Jason Q. McClintic
Aswad Gurjar wrote:
> Hello,
>
> Thanks for help.But I am facing different problem.
>
> I have 421 readings of time and no of requests coming at perticular
> time.Basically I have data with interval of one minute and corresponding no
> of requests.It is discrete in nature.I am collecting data from 9AM to 4PM.But
> some of readings are coming as 0.When I plotted histogram of data I could not
> get shape of any standard distribution.Now,my aim is to find distribution
> which is "best fit" to my data among standard ones.
>
> So there was huge data.That's why I tried to collect data into no of
> bins.That was working properly.Whatever code you have given is working
> properly too.But your code is more efficient.Now,problem comes at next
> stage.When I apply fitdistr() for continuous data or goodfit() for discrete
> data I get following error.I am not able to remove that error.Please help me
> if you can.
> Errors are as follows:
> library(vcd)
> gf<-goodfit(binCount,type= "nbinomial",method= "MinChisq")
> Warning messages:
> 1: NaNs produced in: pnbinom(q, size, prob, lower.tail, log.p)
> 2: NaNs produced in: pnbinom(q, size, prob, lower.tail, log.p)
> 3: NaNs produced in: pnbinom(q, size, prob, lower.tail, log.p)
> 4: NaNs produced in: pnbinom(q, size, prob, lower.tail, log.p)
> 5: NaNs produced in: pnbinom(q, size, prob, lower.tail, log.p)
>> summary(gf)
>
> Goodness-of-fit test for nbinomial distribution
>
> X^2 df P(> X^2)
> Pearson 9.811273 2 0.007404729
> Warning message:
> Chi-squared approximation may be incorrect in: summary.goodfit(gf)
>
> for another distribution:
> gf<-goodfit(binCount,type= "poisson",method= "MinChisq")
> Warning messages:
> 1: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 2: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 3: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 4: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 5: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 6: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 7: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> 8: NA/Inf replaced by maximum positive value in: optimize(chi2, range(count))
> Goodness-of-fit test for poisson distribution
>
> X^2 df P(> X^2)
> Pearson 1.660931e+115 3 0
> Warning message:
> Chi-squared approximation may be incorrect in: summary.goodfit(gf)
>
>
> Aswad
> On 2/10/08, Jason Q. McClintic < [EMAIL PROTECTED]> wrote:
>
> I get the digest, so I apologize if this is a little late.
>
> For your situation (based on the description and what I think your code
> is doing, more on that below), it looks like you are modeling a Poisson
> flow where the number of hits per unit time is a random integer with
> some mean value.
>
> If I understand your code correctly, you are trying to put your data
> into k bins of width f<-(max(V1)-min(V1))/k. In that case I would think
> something like this would work more efficiently:
>
> m<-min(V1);
> k<-floor(1 + log2(length(V1)));
> f<-(max(V1)-min(V1))/k;
> binCount<-NULL;
> for(i in seq(length=k)){
> binIndex<-which((m+(i-1)*f<V1)&(V1<m+i*f));
> binCount[i]<-sum(V2[binIndex]);
> };
>
> where i becomes the index of time intervals.
>
> Hope it helps.
>
> Sincerely,
>
> Jason Q. McClintic
>
> [EMAIL PROTECTED] wrote:
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