[R] MLE packages

[Mohan Ganesalingam]

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.4567 using 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 <mohan using 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]]
>>
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>

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