betabin {aod} | R Documentation |

Fits a beta-binomial generalized linear model accounting for overdispersion in clustered binomial data *(n, y)*.

betabin(formula, random, data, link = c("logit", "cloglog"), phi.ini = NULL, warnings = FALSE, na.action = na.omit, fixpar = list(), hessian = TRUE, control = list(maxit = 2000), ...)

`formula` |
A formula for the fixed effects `b` . The left-hand side of the formula must be of the form
`cbind(y, n - y)` where the modelled probability is `y/n` . |

`random` |
A right-hand formula for the overdispersion parameter(s) φ. |

`link` |
The link function for the mean p: “logit” or “cloglog”. |

`data` |
A data frame containing the response (`n` and `y` ) and explanatory variable(s). |

`phi.ini` |
Initial values for the overdispersion parameter(s) φ. Default to 0.1. |

`warnings` |
Logical to control the printing of warnings occurring during log-likelihood maximization.
Default to `FALSE` (no printing). |

`na.action` |
A function name: which action should be taken in the case of missing value(s). |

`fixpar` |
A list with 2 components (scalars or vectors) of the same size, indicating which parameters are
fixed (i.e., not optimized) in the global parameter vector (b, φ) and the corresponding fixed values.For example, `fixpar = list(c(4, 5), c(0, 0))` means that 4th and 5th parameters of the model are set to 0. |

`hessian` |
A logical. When set to `FALSE` , the hessian and the variances-covariances matrices of the
parameters are not computed. |

`control` |
A list to control the optimization parameters. See `optim` . By default, set the maximum number of iterations to 2000. |

`...` |
Further arguments passed to `optim` . |

For a given cluster *(n, y)*, the model is:

*
y | λ ~ Binomial(n, λ)*

with *λ* following a Beta distribution *Beta(a1, a2)*.

If *B* denotes the beta function, then:

*
P(λ) = λ^{a1 - 1} * (1 - λ)^{a2 - 1} / B(a1, a2)*

*E[λ] = a1 / (a1 + a2)*

*
Var[λ] = a1 * a2 / [(a1 + a2 + 1) * (a1 + a2)^2]*

The marginal beta-binomial distribution is:

*
P(y) = C(n, y) * B(a1 + y, a2 + n - y) / B(a1, a2)*

The function uses the parameterization *
p = a1 / (a1 + a2) = h(X b) = h(η)* and *φ = 1 / (a1 + a2 + 1)*,
where *h* is the inverse of the link function (logit or complementary log-log), *X* is a design-matrix, *b*
is a vector of fixed effects, *η = X b* is the linear predictor and *φ* is the overdispersion
parameter (i.e., the intracluster correlation coefficient, which is here restricted to be positive).

The marginal mean and variance are:

*E[y] = n * p*

*Var[y] = n * p * (1 - p) * [1 + (n - 1) * φ]*

The parameters *b* and *φ* are estimated by maximizing the log-likelihood of the marginal model (using the
function `optim`

). Several explanatory variables are allowed in *b*, only one in *φ*.

An object of formal class “glimML”: see `glimML-class`

for details.

Matthieu Lesnoff matthieu.lesnoff@cirad.fr, Renaud Lancelot renaud.lancelot@cirad.fr

Crowder, M.J., 1978. *Beta-binomial anova for proportions*. Appl. Statist. 27, 34-37.

Griffiths, D.A., 1973. *Maximum likelihood estimation for the beta-binomial distribution and an application
to the household distribution of the total number of cases of disease*. Biometrics 29, 637-648.

Prentice, R.L., 1986. *Binary regression using an extended beta-binomial distribution, with discussion of
correlation induced by covariate measurement errors*. J.A.S.A. 81, 321-327.

Williams, D.A., 1975. *The analysis of binary responses from toxicological experiments involving
reproduction and teratogenicity*. Biometrics 31, 949-952.

`glimML-class`

, `glm`

and `optim`

data(orob2) fm1 <- betabin(cbind(y, n - y) ~ seed, ~ 1, data = orob2) fm2 <- betabin(cbind(y, n - y) ~ seed + root, ~ 1, data = orob2) fm3 <- betabin(cbind(y, n - y) ~ seed * root, ~ 1, data = orob2) # show the model fm1; fm2; fm3 # AIC AIC(fm1, fm2, fm3) summary(AIC(fm1, fm2, fm3), which = "AICc") # Wald test for root effect wald.test(b = coef(fm3), Sigma = vcov(fm3), Terms = 3:4) # likelihood ratio test for root effect anova(fm1, fm3) # model predictions New <- expand.grid(seed = levels(orob2$seed), root = levels(orob2$root)) data.frame(New, predict(fm3, New, se = TRUE, type = "response")) # Djallonke sheep data data(dja) betabin(cbind(y, n - y) ~ group, ~ 1, dja) # heterogeneous phi betabin(cbind(y, n - y) ~ group, ~ group, dja, control = list(maxit = 1000)) # phi fixed to zero in group TREAT betabin(cbind(y, n - y) ~ group, ~ group, dja, fixpar = list(4, 0)) # glim without overdispersion summary(glm(cbind(y, n - y) ~ group, family = binomial, data = dja)) # phi fixed to zero in both groups betabin(cbind(y, n - y) ~ group, ~ group, dja, fixpar = list(c(3, 4), c(0, 0)))

[Package *aod* version 1.2 Index]