Marginal Bivariate Binomial Regression Models
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.
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)
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. |
A list of class bivbinom is returned.
J.K. Lindsey
# 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)))
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