dirmul.old {VGAM} | R Documentation

## Fitting a Dirichlet-Multinomial Distribution

### Description

Fits a Dirichlet-multinomial distribution to a matrix of non-negative integers.

### Usage

    dirmul.old(link = "loge", ialpha = 0.01, parallel = FALSE, zero = NULL)

### Arguments

`link` | Link function applied to each of the M (positive) shape parameters alpha_j for j=1,…,M. See `Links` for more choices. Here, M is the number of columns of the response matrix.
`ialpha` | Numeric vector. Initial values for the `alpha` vector. Must be positive. Recycled to length M.
`parallel` | A logical, or formula specifying which terms have equal/unequal coefficients.
`zero` | An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2,...,M}.

### Details

The Dirichlet-multinomial distribution, which is somewhat similar to a Dirichlet distribution, has probability function

P(Y_1=y_1,…,Y_M=y_M) = C_{y_1,…,y_M}^{2y_{*}} Gamma(alpha_+) / Gamma( 2y_* + alpha_+) prod_{j=1}^M [ Gamma( y_j+ alpha_j) / Gamma( alpha_j)]

for alpha_j > 0, alpha_+ = alpha_1 + \cdots + alpha_M, and 2y_* = y_1 + \cdots + y_M. Here, C_b^a means “a choose b” and refers to combinations (see `choose`). The (posterior) mean is

E(Y_j) = (y_j + alpha_j) / (2y_{*} + alpha_+)

for j=1,…,M, and these are returned as the fitted values as a M-column matrix.

### Value

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

### Note

The response should be a matrix of non-negative values. Convergence seems to slow down if there are zero values. Currently, initial values can be improved upon.

This function is almost defunct and may be withdrawn soon. Use `dirmultinomial` instead.

### Author(s)

Thomas W. Yee

### References

Lange, K. (2002) Mathematical and Statistical Methods for Genetic Analysis, 2nd ed. New York: Springer-Verlag.

Forbes, C., Evans, M., Hastings, N. and Peacock, B. (2011) Statistical Distributions, Hoboken, NJ, USA: John Wiley and Sons, Fourth edition.

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 θ-correction in forensic genetics. Theoretical Population Biology, 78, 200–210.

### See Also

`dirmultinomial`, `dirichlet`, `betabinomialff`, `multinomial`.

### Examples

    # Data from p.50 of Lange (2002)
    alleleCounts <- c(2, 84, 59, 41, 53, 131, 2, 0,
           0, 50, 137, 78, 54, 51, 0, 0,
           0, 80, 128, 26, 55, 95, 0, 0,
           0, 16, 40, 8, 68, 14, 7, 1)
    dim(alleleCounts) <- c(8, 4)
    alleleCounts <- data.frame(t(alleleCounts))
    dimnames(alleleCounts) <- list(c("White","Black","Chicano","Asian"),
                        paste("Allele", 5:12, sep = ""))

    set.seed(123)  # @initialize uses random numbers
    fit <- vglm(cbind(Allele5,Allele6,Allele7,Allele8,Allele9,
                      Allele10,Allele11,Allele12) ~ 1, dirmul.old,
                 trace = TRUE, crit = "c", data = alleleCounts)

    (sfit <- summary(fit))
    vcov(sfit)
    round(eta2theta(coef(fit), fit@misc$link, fit@misc$earg), digits = 2)  # not preferred
    round(Coef(fit), digits = 2)  # preferred
    round(t(fitted(fit)), digits = 4)  # 2nd row of Table 3.5 of Lange (2002)
    coef(fit, matrix = TRUE)

    pfit <- vglm(cbind(Allele5,Allele6,Allele7,Allele8,Allele9,
                       Allele10,Allele11,Allele12) ~ 1,
                 dirmul.old(parallel = TRUE), trace = TRUE,
                 data = alleleCounts)
    round(eta2theta(coef(pfit, matrix = TRUE), pfit@misc$link,
                    pfit@misc$earg), digits = 2)  # 'Right' answer
    round(Coef(pfit), digits = 2)  # 'Wrong' answer due to parallelism constraint

[Package VGAM version 1.0-4 Index]