Bivariate Beta-binomial Regression

Usage

biv.betab(freq, x=NULL, p, depend=T, print.level=0,
typsiz=abs(p), ndigit=10, gradtol=0.00001, stepmax=10*sqrt(p%*%p),
steptol=0.00001, iterlim=100, fscale=1)

Arguments

freq A matrix containing four columns corresponding to 00, 01, 10, and 11 responses.
x A matrix of explanatory variables, containing pairs of columns, one for each response, and the same number of rows as freq.
p Initial parameter estimates: intercept, dependence (if depend is TRUE, and one for each pair of columns of x.
depend If FALSE, the independence (logistic) model is fitted.
other Arguments for nlm.

Description

biv.betab fits dependent (logit) linear regression models to a bivariate beta-binomial distribution.

Value

A list of class bivbetab is returned.

Author(s)

J.K. Lindsey

Examples

y <- matrix(  c( 2, 1, 1,13,
		 4, 1, 3, 5,
		 3, 3, 1, 4,
		15, 8, 1, 6),ncol=4,byrow=T)
first <- c(0,0,1,1)
second <- c(0,1,0,1)
self <- cbind(first,second)
other <- cbind(second,first)
biv.betab(y,cbind(self,other),p=c(-1,2,1,1))
# independence
biv.betab(y,cbind(self,other),p=c(-1,1,1),dep=F)


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