Hi Bert, thanks for the quick reply. I spent a while searching before I posted, and also read through the documentation for the mle fn and the maxLik and bbmle packages. As you say, it seems likely I'm reinventing something standard, but nothing I can find quite seems to do what I need. Hence posting on the mailing list... .
best wishes, Mohan On Mon, 21 Oct 2019 at 16:40, Bert Gunter <bgunter.4...@gmail.com> wrote: > Are you familiar with R resources you can search? > > 1. CRAN task views: > https://cran.r-project.org/web/views/ > > 2. For searching: https://rseek.org/ > Searching on "maximum likelihood" there appeared to bring up relevant > resources. > > 3. RStudio resources: https://education.rstudio.com/ > Note: RStudio is a private company that is not part of the R Foundation, > but may have useful programming resources for you. > > 4. Tons of online tutorials: Just search! > > I have not looked at your code in any detail, but I'd be willing to bet > you're trying to reinvent a square wheel. > > Cheers, > Bert > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Mon, Oct 21, 2019 at 8:21 AM Mohan Ganesalingam <mo...@what3words.com> > wrote: > >> I'm fairly new to R. The language is amazing, but I'm having trouble >> navigating packages. I have a solution that handles the problems I'm >> working on, but I don't know if it could be solved more cleanly with mle, >> bbmle, maxLik, etc.. >> >> Here's an example problem first. I have run many WAV files through voice >> recognition software; the software returns 50 hypotheses for each, >> together >> with scores S_{ni} indicating how 'good' the i^th hypothesis is. I want to >> map the S_{ni} to a probability distribution. So I'm using MLE to fit a >> function f that maps scores to logs of relative probabilities. This means >> maximising >> >> \sum_n[ f(S_{nc_n}) - \log \sum_i \exp f(S_{ni}) ] >> >> where c_n is the index of the correct hypothesis for the n^th sample. >> >> Here's the code: >> >> ave_log_likelihood = function(f, scores) { >> def = scores %>% filter(Sc > 0) >> log_likelihoods = with(def, f(Sc) - matrixStats::rowLogSumExps(f(S), >> na.rm = T)) >> return(mean(log_likelihoods)) >> } >> >> nlopts = list(algorithm = "NLOPT_LN_BOBYQA", maxeval = 500, print_level = >> 0) >> >> best_linear_fit = function(scores) { >> res <- nloptr(c(0.01), >> function(a) -ave_log_likelihood(function(x) (a * x), >> scores), >> opts = nlopts) >> return (data.frame(log_likelihood = -res$objective, slope = >> res$solution, >> doubling = log(2)/res$solution)) >> } >> >> >> Now, I need to write a lot of variants of this with different objectives >> and with different classes of function. But there's a lot of verbiage in >> best_linear_fit which would currently be copy/pasted. Also, as written it >> makes it messy to fit on training data and then evaluate on test data. >> >> I'd appreciate any advice on packages that might make it easier to write >> this more cleanly, ideally using the idioms used in `lm`, etc., such as >> formulae and `predict`. (Any pointers on writing cleaner R code would also >> be lovely!) >> >> Thanks in advance; >> Mohan >> >> [[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. >> > [[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.
