The dirichlet() function in the hyper2 package

dirichlet

function (powers, alpha) 
{
    if (!xor(missing(powers), missing(alpha))) {
        stop("supply exactly one of powers, alpha")
    }
    if (missing(powers)) {
        powers <- alpha - 1
    }
    np <- names(powers)
    if (is.null(np)) {
        stop("supply a named vector")
    }
    hyper2(as.list(np), d = powers) - hyper2(list(np), d = sum(powers))
}

To cite the hyper2 package in publications, please use Hankin (2017). We say that non-negative p₁, …, p_n satisfying ∑p_i = 1 follow a Dirichlet distribution, and write 𝒟(p₁, …, p_n), if their density function is

for some parameters α_i > 0. It is interesting in the current context because it is conjugate to the multinomial distribution; specifically, given a Dirichlet prior 𝒟(α₁, …, α_n) and likelihood function $\mathcal{L}(p_1,\ldots,p_n)\propto\prod_{i=1}^n p_i^{\alpha_i}$ then the posterior is 𝒟(α₁ + a₁, …, α_n + a_n).

In the context of the hyper2 package, function dirichlet() presents some peculiarities, discussed here.

A simple example

Consider a three-way trial between players a, b, and c with eponymous Bradley-Terry strengths. Suppose that, out of 4 + 5 + 3 = 12 trials, a wins 4, b wins 5, and c wins 3. Then an appropriate likelihood function would be:

dirichlet(c(a=4,b=5,c=3))

## log(a^4 * (a + b + c)^-12 * b^5 * c^3)

Note the (a+b+c)^-11 term, which is not strictly necessary according the Dirichlet density function above which has no such term. However, we see that the returned likelihood function is ${\propto} p_{a\vphantom{abc}}^4p_{b\vphantom{abc}}^5p_{c\vphantom{abc}}^3$ (because p_a + p_b + p_c = 1).

(L1 <- dirichlet(c(a=4,b=5,c=3)))

## log(a^4 * (a + b + c)^-12 * b^5 * c^3)

Now suppose we observe three-way trials between b, c, and d:

(L2 <- dirichlet(c(b=1,c=1,d=8)))

## log( b * (b + c + d)^-10 * c * d^8)

The overall likelihood function would be L₁ + L₂:

L1+L2

## log(a^4 * (a + b + c)^-12 * b^6 * (b + c + d)^-10 * c^4 * d^8)

maxp(L1+L2)

##          a          b          c          d 
## 0.09090991 0.10909114 0.07272658 0.72727237

Observe the dominance of competitor d, reasonable on the grounds that d won 8 of the 10 trials against b and c; and a, b, c are more or less evenly matched [chi square test, p = 0.94]. Observe further that we can include four-way observations easily:

(L3 <- dirichlet(c(a=4,b=3,c=2,d=6)))

## log(a^4 * (a + b + c + d)^-15 * b^3 * c^2 * d^6)

L1+L2+L3

## log(a^8 * (a + b + c)^-12 * (a + b + c + d)^-15 * b^9 * (b + c + d)^-10
## * c^6 * d^14)

Package idiom L1+L2+L3 operates as expected because dirichlet() includes the denominator. Sometimes we see situations in which a competitor does not win any trials. Consider the following:

(L4 <- dirichlet(c(a=5,b=3,c=0,d=3)))

## log(a^5 * (a + b + c + d)^-11 * b^3 * d^3)

Above, we see that c won no trials and is not present in the numerator of the expression. However, L4 is informative about competitor c:

maxp(L4)

##            a            b            c            d 
## 4.546526e-01 2.727224e-01 1.020077e-06 2.726240e-01

We see that the maximum likelihood estimate for c is zero (to within numerical tolerance). Further, we may reject the hypothesis that $p_c=\frac{1}{4}$ [which might be a reasonable consequence of the assumption that all four competitors have the same strength]:

specificp.test(L4,'c',0.25)

## 
##  Constrained support maximization
## 
## data:  H
## null hypothesis: sum p_i=1, c = 0.25
## null estimate:
##         a         b         c         d 
## 0.3122093 0.2162741 0.2500000 0.2215167 
## (argmax, constrained optimization)
## Support for null:  -14.93581 + K
## 
## alternative hypothesis:  sum p_i=1 
## alternative estimate:
##            a            b            c            d 
## 4.546526e-01 2.727224e-01 1.020077e-06 2.726240e-01 
## (argmax, free optimization)
## Support for alternative:  -11.738 + K
## 
## degrees of freedom: 1
## support difference = 3.197811
## p-value: 0.01144022 (two sided)

However, observe that we cannot reject the equality hypothesis, that is, $p_a=p_b=p_c=p_d=\frac{1}{4}$:

equalp.test(L4)

## 
##  Constrained support maximization
## 
## data:  L4
## null hypothesis: a = b = c = d
## null estimate:
##    a    b    c    d 
## 0.25 0.25 0.25 0.25 
## (argmax, constrained optimization)
## Support for null:  -15.24924 + K
## 
## alternative hypothesis:  sum p_i=1 
## alternative estimate:
##            a            b            c            d 
## 4.546526e-01 2.727224e-01 1.020077e-06 2.726240e-01 
## (argmax, free optimization)
## Support for alternative:  -11.738 + K
## 
## degrees of freedom: 3
## support difference = 3.511242
## p-value: 0.07118459

References

Hankin, R. K. S. 2017. “Partial Rank Data with the hyper2 Package: Likelihood Functions for Generalized Bradley-Terry Models.” The R Journal 9 (2): 429–39.

- A simple example
- References