VGAM (version 0.9-1)

dirmultinomial: Fitting a Dirichlet-Multinomial Distribution

Description

Fits a Dirichlet-multinomial distribution to a matrix response.

Usage

dirmultinomial(lphi = "logit", iphi = 0.10, parallel = FALSE, zero = "M")

Arguments

lphi

Link function applied to the $\phi$ parameter, which lies in the open unit interval $(0,1)$. See Links for more choices.

iphi

Numeric. Initial value for $\phi$. Must be in the open unit interval $(0,1)$. If a failure to converge occurs then try assigning this argument a different value.

parallel

A logical (formula not allowed here) indicating whether the probabilities $\pi_1,\ldots,\pi_{M-1}$ are to be equal via equal coefficients. Note $\pi_M$ will generally be different from the other probabilities. Setting parallel = TRUE

zero

An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set ${1,2,\ldots,M}$. If the character "M" then this means the numerical value $M$, which corresponds to

Value

• An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

  If the model is an intercept-only model then @misc (which is a list) has a component called shape which is a vector with the $M$ values $\pi_j(1/\phi-1)$.

Warning

This VGAM family function is prone to numerical problems, especially when there are covariates.

Details

The Dirichlet-multinomial distribution arises from a multinomial distribution where the probability parameters are not constant but are generated from a multivariate distribution called the Dirichlet distribution. The Dirichlet-multinomial distribution has probability function $$P(Y_1=y_1,\ldots,Y_M=y_M) = {N_{*} \choose {y_1,\ldots,y_M}} \frac{ \prod_{j=1}^{M} \prod_{r=1}^{y_{j}} (\pi_j (1-\phi) + (r-1)\phi)}{ \prod_{r=1}^{N_{*}} (1-\phi + (r-1)\phi)}$$ where $\phi$ is the over-dispersion parameter and $N_{*} = y_1+\cdots+y_M$. Here, $a \choose b$ means ``$a$ choose $b$'' and refers to combinations (see choose). The above formula applies to each row of the matrix response. In this VGAM family function the first $M-1$ linear/additive predictors correspond to the first $M-1$ probabilities via $$\eta_j = \log(P[Y=j]/ P[Y=M]) = \log(\pi_j/\pi_M)$$ where $\eta_j$ is the $j$th linear/additive predictor ($\eta_M=0$ by definition for $P[Y=M]$ but not for $\phi$) and $j=1,\ldots,M-1$. The $M$th linear/additive predictor corresponds to lphi applied to $\phi$.

Note that $E(Y_j) = N_* \pi_j$ but the probabilities (returned as the fitted values) $\pi_j$ are bundled together as a $M$-column matrix. The quantities $N_*$ are returned as the prior weights.

The beta-binomial distribution is a special case of the Dirichlet-multinomial distribution when $M=2$; see betabinomial. It is easy to show that the first shape parameter of the beta distribution is $shape1=\pi(1/\phi-1)$ and the second shape parameter is $shape2=(1-\pi)(1/\phi-1)$. Also, $\phi=1/(1+shape1+shape2)$, which is known as the intra-cluster correlation coefficient.

References

Paul, S. R., Balasooriya, U. and Banerjee, T. (2005) Fisher information matrix of the Dirichlet-multinomial distribution. Biometrical Journal, 47, 230--236.

Tvedebrink, T. (2010) Overdispersion in allelic counts and $\theta$-correction in forensic genetics. Theoretical Population Biology, 78, 200--210.

See Also

dirmul.old, betabinomial, betabinomial.ab, dirichlet, multinomial.

Examples

nn <- 10; M <- 5
ydata <- data.frame(round(matrix(runif(nn * M, max = 10), nn, M))) # Integer counts
colnames(ydata) <- paste("y", 1:M, sep = "")

fit <- vglm(cbind(y1, y2, y3, y4, y5) ~ 1, dirmultinomial, ydata, trace = TRUE)
head(fitted(fit))
depvar(fit) # Sample proportions
weights(fit, type = "prior", matrix = FALSE) # Total counts per row

ydata <- transform(ydata, x2 = runif(nn))
fit <- vglm(cbind(y1, y2, y3, y4, y5) ~ x2, dirmultinomial, ydata, trace = TRUE)
# This does not work:
Coef(fit)
coef(fit, matrix = TRUE)
(sfit <- summary(fit))
vcov(sfit)