find total variation distance between multinomial distributions in r - r

Question

I am comparing Bayes estimators to MLE in multinomial distributions. I am drawing random samples using rmultinom from a particular multinomial distribution using
rmultinom(400, size = 30, prob = c(5,7,10,8,14,10,15,12,10,9))
For each of the 400 samples, I compute the MLE and Bayes estimators for the ten probability parameters. I now want to find in each case the total variation distance between the true distribution and the one defined by the estimators.
Since for size 30 and 10 bins there are over 200 million possible arrangements, I don't think that using the theoretical definition is a good idea.
The package distrEx has a function "TotalVarDist()", but it can only be used with distributions defined in the distr package, and multinomial is not one of them. There are directions for defining them (see here and here) but the options are either to define a discrete distribution by explicitly listing the support (again, I don't think this is a good option since the support has a size of over 200 million) or starting from scratch using the same methods as how the distr package was created, which is beyond my current ability.
Any thoughts on how to do this, either using the packages mentioned or in a completely different way?

Answer 1

My answer is about how to calculate this using base R.
We have two multinomial parameter vectors, θ and η. The total variation distance is equivalent to P_θ(E) - P_η(E), where E={ω | P_θ({ω})>P_η({ω})}, and ω is a vector of sample counts.
I know of two ways to evaluate P(E) in base R. One is a very simple simulation-based method. The other reframes the problem in terms of a linear combination of the counts, which is approximately normally distributed, and uses the pnorm function.
Simulation-based method
You simulate samples from each distribution, check whether they're in E using the probability mass functions, and count how often they are. I'll go through an example here. We'll assume the true distribution from your question:
unnormalized.true <- c(5,7,10,8,14,10,15,12,10,9)
true <- unnormalized.true / sum(unnormalized.true)
We'll draw a sample and estimate a new distribution using a Bayes estimator:
set.seed(921)
result <- as.vector(rmultinom(1, size = 30, prob = true))
result
## [1] 3 6 2 0 5 3 3 4 1 3
dirichlet <- (result+1)/(30+length(true))
Calculating the probability of E under the true distribution:
set.seed(939)
true.dist <- rmultinom(10^6, 30, true)
p.true.e <- mean(apply(true.dist, 2, function(x)
dmultinom(x, 30, true) - dmultinom(x, 30, dirichlet) > 0))
Calculating the probability of E under the estimated distribution from the Bayes estimator:
dirichlet.dist <- rmultinom(10^6, 30, dirichlet)
p.dirichlet.e <- mean(apply(dirichlet.dist, 2, function(x)
dmultinom(x, 30, true) - dmultinom(x, 30, dirichlet) > 0))
And we can subtract to get the total variation distance.
p.true.e - p.dirichlet.e
## [1] 0.83737
Repeating this with the maximum likelihood estimate, we get a comparison of the estimators.
mle <- result/30
mle.dist <- rmultinom(10^6, 30, mle)
p.true.e2 <- mean(apply(true.dist, 2, function(x)
dmultinom(x, 30, true) - dmultinom(x, 30, mle) > 0))
p.mle.e2 <- mean(apply(mle.dist, 2, function(x)
dmultinom(x, 30, true) - dmultinom(x, 30, mle) > 0))
p.true.e2 - p.mle.e2
## [1] 0.968301
(edited to fix a serious mistake. Previously I had re-used p.true.e in the comparison with the MLE. I forgot that the event E is defined in terms of the estimated distribution.)
Normal approximation
I think this method is actually more accurate than the simulation based method, despite the normal approximation. As you'll see, we're not taking a normal approximation to the multinomial counts, which would be unlikely to be accurate for n=30. We're taking a normal approximation to a linear combination of these counts, which is close to normal. The weakness of this method will turn out to be that it can't handle zero probabilities in the estimated distribution. That's a real problem, since handling zeros gracefully is, to me, part of the point of using total variation distance rather than Kullback-Leibler divergence. But here it is.
The following derivation yields a restatement of E:
Define
where N_i is one cell of the multinomial sample, and
Then, E is the event that L>0.
The reason we have a problem with a zero probability is that it causes one of the λ_i's to be infinite.
I want to verify that L is close to normally distributed, in the example from before. I'll do that by getting samples from the distribution of L, using the previous multinomial simulation:
lambda <- log(true/dirichlet)
L.true.dist <- apply(true.dist, 2, function(x) sum(lambda*x))
L.dirichlet.dist <- apply(dirichlet.dist, 2, function(x) sum(lambda*x))
Note that I'm doing the comparison between the true distribution and the Bayes estimated distribution. I can't do the one with the MLE, because my sample had a zero count.
Plotting the distribution of L and comparing to a normal fit:
par(mfrow=c(1,2))
L.true.dist.hist <- hist(L.true.dist)
L.true.dist.fit <- function(x)
length(L.true.dist) * diff(L.true.dist.hist$breaks)[1] *
dnorm(x, mean(L.true.dist), sd=sd(L.true.dist))
curve(L.true.dist.fit, add=TRUE, n=1000, col='red')
L.dirichlet.dist.hist <- hist(L.dirichlet.dist)
L.dirichlet.dist.fit <- function(x)
length(L.dirichlet.dist) * diff(L.dirichlet.dist.hist$breaks)[1] *
dnorm(x, mean(L.dirichlet.dist), sd=sd(L.dirichlet.dist))
curve(L.dirichlet.dist.fit, add=TRUE, n=1000, col='red')
par(mfrow=c(1,1))
The distribution of L appears normal. So, instead of using simulations, we can just use pnorm. However, we need to calculate the mean and standard deviation of L. This can be done as follows.
The mean of L is
where p_i is the cell probability of cell i in the distribution p. The variance is
where
is the covariance matrix of the multinomial distribution. I'll calculate these moments for this example, and check them against the empirical moments in the simulation. First, for the distribution of L under the true distribution:
n <- 30
k <- length(true)
mean.L.true <- sum(lambda * n * true)
# Did we get the mean right?
c(mean.L.true, mean(L.true.dist))
## [1] 3.873509 3.875547
# Covariance matrix assuming the true distribution
sigma.true <- outer(1:k, 1:k, function(i,j)
ifelse(i==j, n*true[i]*(1-true[i]), -n*true[i]*true[j]))
var.L.true <- t(lambda) %*% sigma.true %*% lambda
# Did we get the standard deviation right?
c(sqrt(var.L.true), sd(L.true.dist))
## [1] 2.777787 2.776945
Then, the mean and variance of L under the Bayes estimate of the distribution:
mean.L.dirichlet <- sum(lambda * n * dirichlet)
# Did we get the mean right?
c(mean.L.dirichlet, mean(L.dirichlet.dist))
## [1] -3.893836 -3.895983
# Covariance matrix assuming the estimated distribution
sigma.dirichlet <- outer(1:k, 1:k, function(i,j)
ifelse(i==j, n*dirichlet[i]*(1-dirichlet[i]), -n*dirichlet[i]*dirichlet[j]))
var.L.dirichlet <- t(lambda) %*% sigma.dirichlet %*% lambda
# Did we get the standard deviation right?
c(sqrt(var.L.dirichlet), sd(L.dirichlet.dist))
## [1] 2.796348 2.793421
With these in hand, we can calculate the total variation distance with pnorm:
pnorm(0, mean.L.true, sd=sqrt(var.L.true), lower.tail=FALSE) -
pnorm(0, mean.L.dirichlet, sd=sqrt(var.L.true), lower.tail=FALSE)
## [1] 0.8379193
# Previous result was 0.83737
We get three digits of agreement with the simulation.
I don't know of any easy way to extend the normal approximation method to handle zero probabilities, though. I had an idea, but I got stuck trying to calculate the covariance matrix of the counts conditional on a specific cell having 0 count. I could share my progress if you think you could make something of it.