Data has no negative values. Values range from 0.001 to 1.01. Following is the summary, in case that helps:

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Min. 1st Qu. Median Mean 3rd Qu. Max. 0.0010 0.8126 0.8536 0.8464 0.8888 1.0180 SD: 0.07489977 Any clue? On Thu, Sep 15, 2016 at 10:32 PM, William Dunlap <wdun...@tibco.com> wrote: > Does the data contain non-positive values? > > > out <- mixtools::gammamixEM(as.numeric(0:100), lambda = c(1, 1, 1)/3, > verb = TRUE) > iteration = 1 log-lik diff = NaN log-lik = NaN > Error in while (diff > epsilon && iter < maxit) { : > missing value where TRUE/FALSE needed > > > Bill Dunlap > TIBCO Software > wdunlap tibco.com > > On Thu, Sep 15, 2016 at 3:04 PM, Aanchal Sharma < > aanchalsharma...@gmail.com> wrote: > >> I am using a function gammamixEM where it does it by default. I do not >> have >> the option to change it. >> Conceptually, what can make the algorithm not able to calculate likelihood >> value at all (and hence log-lik=Nan)? Is there sth wrong with the data? >> Under what conditions does it happen? >> >> On Wed, Sep 14, 2016 at 8:04 PM, Duncan Murdoch <murdoch.dun...@gmail.com >> > >> wrote: >> >> > On 14/09/2016 4:46 PM, Aanchal Sharma wrote: >> > >> >> Hi, >> >> >> >> I am trying to fit Gamma mixture model to my data (residual values >> >> obtained >> >> after fitting Generalized linear Model) using gammamixEM. It is part of >> >> the >> >> script which does it for multiple datasets in loop. The code is running >> >> fine for some datasets but it terminates for some giving following >> error: >> >> >> >> " iteration = 1 log-lik diff = NaN log-lik = NaN >> >> Error in while (diff > epsilon && iter < maxit) { : >> >> missing value where TRUE/FALSE needed" >> >> >> >> Seems like EM is not able to calculate log-lik value (NaN) at the first >> >> iteration itself. any idea why that can happen? >> >> It works fine for the other genes in the loop. Tried looking for >> >> difference >> >> in the inputs, but could not come up with anything striking. >> >> >> >> >> > THere are lots of ways to get NaN in numerical calculations. A common >> > one if you are using log() to calculate log likelihoods is that rounding >> > error gives you a negative likelihood, and then log(lik) comes out to >> NaN. >> > >> > You just need to look really closely at each step of your calculations. >> > Avoid using log(); use the functions that build it in (e.g. instead of >> > log(dnorm(x)), use dnorm(x, log = TRUE)). >> > >> > Duncan Murdoch >> > >> > >> >> >> -- >> Anchal Sharma, PhD >> Postdoctoral Fellow >> 195, Little Albany street, >> Cancer Institute of New Jersey >> Rutgers University >> NJ-08901 >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posti >> ng-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > -- Anchal Sharma, PhD Postdoctoral Fellow 195, Little Albany street, Cancer Institute of New Jersey Rutgers University NJ-08901 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.