CODEWITHSUNDEEP
rlog-likelihood
Wave
Wave

Reputation: 1266

Sample from a custom likelihood function

I have the following likelihood function which I used in a rather complex model (in practice on a log scale):

library(plyr)
dcustom=function(x,sd,L,R){
    R. = (log(R) - log(x))/sd
    L. = (log(L) - log(x))/sd
    ll = pnorm(R.) - pnorm(L.)
    return(ll) 
}

df=data.frame(Range=seq(100,500),sd=rep(0.1,401),L=200,U=400)
df=mutate(df, Likelihood = dcustom(Range, sd,L,U))

with(df,plot(Range,Likelihood,type='l'))
abline(v=200)
abline(v=400)

In this function, the sd is predetermined and L and R are "observations" (very much like the endpoints of a uniform distribution), so all 3 of them are given. The above function provides a large likelihood (1) if the model estimate x (derived parameter) is in between the L-R range, a smooth likelihood decrease (between 0 and 1) near the bounds (of which the sharpness is dependent on the sd), and 0 if it is too much outside.

This function works very well to obtain estimates of x, but now I would like to do the inverse: draw a random x from the above function. If I would do this many times, I would generate a histogram that follows the shape of the curve plotted above.

The ultimate goal is to do this in C++, but I think it would be easier for me if I could first figure out how to do this in R.

There's some useful information online that helps me start (http://matlabtricks.com/post-44/generate-random-numbers-with-a-given-distribution, https://stats.stackexchange.com/questions/88697/sample-from-a-custom-continuous-distribution-in-r) but I'm still not entirely sure how to do it and how to code it.

I presume (not sure at all!) the steps are:

  1. transform likelihood function into probability distribution
  2. calculate the cumulative distribution function
  3. inverse transform sampling

Is this correct and if so, how do I code this? Thank you.

Upvotes: 2

Views: 847

Answers (1)

Daniel
Daniel

Reputation: 2229

One idea might be to use the Metropolis Hasting Algorithm to obtain a sample from the distribution given all the other parameters and your likelihood. 

# metropolis hasting algorithm
 set.seed(2018)
 n_sample <- 100000
 posterior_sample <- rep(NA, n_sample)
 x <- 300 # starting value: I chose 300 based on your likelihood plot
 for (i in 1:n_sample){
     lik <- dcustom(x = x, sd = 0.1, L = 200, R =400)
     # propose a value for x (you can adjust the stepsize with the sd)
     x.proposed <-  x + rnorm(1, 0, sd = 20)
     lik.proposed <- dcustom(x = x.proposed, sd = 0.1, L = 200, R = 400)
     r <- lik.proposed/lik # this is the acceptance ratio
         # accept new value with probablity of ratio 
         if (runif(1) < r) {
         x <- x.proposed
     posterior_sample[i] <- x
     }
    }

 # plotting the density  
 approximate_distr <- na.omit(posterior_sample) 
 d <- density(approximate_distr)
 plot(d, main = "Sample from distribution")
 abline(v=200)
 abline(v=400)

enter image description here

# If you now want to sample just a few values (for example, 5) you could use 
 sample(approximate_distr,5)
#[1] 281.7310 371.2317 378.0504 342.5199 412.3302

Upvotes: 2

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