I'd suggest you do some leg-work and figure out why you are getting values >1.
If your algorithm is motivated by some approximation then a min() or pmin()
*might* be the right fix, but if there are no approximations you may need to
start debugging properly to see why you are getting an out of bounds value.
Since there's no random number generation involved, I'd hesitate to just throw
out the result without knowing its source. Also keep in mind the limitations of
floating point arithmetic if you expect alpha*d^beta to be small.
Michael
On Nov 29, 2011, at 6:58 PM, Sarah Goslee <sarah.gos...@gmail.com> wrote:
> On Tue, Nov 29, 2011 at 6:55 PM, Gyanendra Pokharel
> <gyanendra.pokha...@gmail.com> wrote:
>> yes, log of negative number is undefined and R also do the same and produces
>> NaNs. Here I want to reject the value of exp(-alpha*d^(-beta)) when greater
>> than 1, and want to run the loop otherwise.
>> Thanks
>
> Then just add another if() statement checking for that condition.
>
>> On Tue, Nov 29, 2011 at 6:48 PM, Sarah Goslee <sarah.gos...@gmail.com>
>> wrote:
>>>
>>>> Here p[i] <- 1 - exp(-alpha*d^(-beta))> so, log(p[i]) produces NaNs
>>>> when exp(-alpha*d^(-beta)) is greater than 1.> How can I remove it.After
>>>> generating the out put we can omit it, but the> problem is different.
>>>
>>> Wait... you're complaining that you can't take the natural log of a
>>> negative
>>> number in R?
>>>
>>> You can't do that anywhere. What do you expect to happen? The log of a
>>> negative number IS NaN.
>>>
>>> Sarah
>>> On Tue, Nov 29, 2011 at 6:28 PM, Gyanendra Pokharel
>>> <gyanendra.pokha...@gmail.com> wrote:
>>>> I have following code:
>>>> loglikelihood <- function(alpha,beta= 0.1){
>>>> loglh<-0
>>>> d<-0
>>>> p<-0
>>>> k<-NULL
>>>> data<-read.table("epidemic.txt",header = TRUE)
>>>> attach(data, warn.conflicts = F)
>>>> k <-which(inftime==1)
>>>> d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta)
>>>> p<-1 - exp(-alpha*d)
>>>> for(i in 1:100){
>>>> if(i!=k){
>>>> if(inftime[i]==0){
>>>> loglh<-loglh +log(1-p[i])
>>>> }
>>>> if(inftime[i]==2){
>>>> loglh<-loglh + log(p[i])
>>>> }
>>>> }
>>>> }
>>>> return(loglh)
>>>> }
>>>> Here p[i] <- 1 - exp(-alpha*d^(-beta))
>>>> so, log(p[i]) produces NaNs when exp(-alpha*d^(-beta)) is greater than
>>>> 1.
>>>> How can I remove it.After generating the out put we can omit it, but the
>>>> problem is different.
>>>>
>>>> On Tue, Nov 29, 2011 at 5:22 PM, Gyanendra Pokharel <
>>>> gyanendra.pokha...@gmail.com> wrote:
>>>>
>>>>> No, that,s not a problem Michael,
>>>>> I have following code:
>>>>> loglikelihood <- function(alpha,beta= 0.1){
>>>>> loglh<-0
>>>>> d<-0
>>>>> p<-0
>>>>> k<-NULL
>>>>> data<-read.table("epidemic.txt",header = TRUE)
>>>>> attach(data, warn.conflicts = F)
>>>>> k <-which(inftime==1)
>>>>> d <- (sqrt((x-x[k])^2+(y-y[k])^2))^(-beta)
>>>>> p<-1 - exp(-alpha*d)
>>>>> for(i in 1:100){
>>>>> if(i!=k){
>>>>> if(inftime[i]==0){
>>>>> loglh<-loglh +log(1-p[i])
>>>>> }
>>>>> if(inftime[i]==2){
>>>>> loglh<-loglh + log(p[i])
>>>>> }
>>>>> }
>>>>> }
>>>>> return(loglh)
>>>>> }
>>>>> Here p[i] <- 1 - exp(-alpha*d^(-beta))
>>>>> so, log(p[i]) produces NaNs when exp(-alpha*d^(-beta)) is greater than
>>>>> 1.
>>>>> How can I remove it.After generating the out put we can omit it, but
>>>>> the
>>>>> problem is different.
>>>>>
>>>>>
>>>
>>> --
>>> Sarah Goslee
>>> http://www.functionaldiversity.org
>>
>>
>
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