biv.binom: Marginal Bivariate Binomial Regression Models

In repeated: Non-Normal Repeated Measurements Models

Marginal Bivariate Binomial Regression Models

Description

biv.binom fits (logit) linear regression models to a marginal bivariate binomial distribution. The covariates must be of length K, that is the number of 2x2 tables.

Usage

biv.binom(
  freq,
  marg1 = ~1,
  marg2 = ~1,
  interaction = ~1,
  pmarg1 = 1,
  pmarg2 = 1,
  pinteraction = 1,
  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 four-column matrix containing K 2x2 frequency tables.
marg1	The model formula for the first margin.
marg2	The model formula for the second margin.
interaction	The model formula for the interaction.
pmarg1	Initial parameter estimates for the first margin regression.
pmarg2	Initial parameter estimates for the second margin regression.
pinteraction	Initial parameter estimates for the interaction regression.
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 bivbinom is returned.

Author(s)

J.K. Lindsey

Examples

# 5 2x2 tables
Freq <- matrix(rpois(20,10),ncol=4)
x <- c(6,8,10,12,14)
print(z <- biv.binom(Freq,marg1=~x,marg2=~x,inter=~x,pmarg1=c(-2,0.08),
	pmarg2=c(-2,0.1),pinter=c(3,0)))

repeated documentation built on Aug. 8, 2023, 5:07 p.m.