biv.betab: Bivariate Beta-binomial Regression Models

In repeated: Non-Normal Repeated Measurements Models

Bivariate Beta-binomial Regression Models

Description

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

Usage

biv.betab(
  freq,
  x = NULL,
  p,
  depend = TRUE,
  print.level = 0,
  typsize = abs(p),
  ndigit = 10,
  gradtol = 1e-05,
  stepmax = 10 * sqrt(p %*% p),
  steptol = 1e-05,
  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.
print.level	Arguments for nlm.
typsize	Arguments for nlm.
ndigit	Arguments for nlm.
gradtol	Arguments for nlm.
stepmax	Arguments for nlm.
steptol	Arguments for nlm.
iterlim	Arguments for nlm.
fscale	Arguments for nlm.

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=TRUE)
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=FALSE)

repeated documentation built on Sept. 30, 2024, 9:16 a.m.