mets (version 1.0)

binomial.twostage: Fits Clayton-Oakes or bivariate Plackett (OR) models for binary data using marginals that are on logistic form. If clusters contain more than two times, the algoritm uses a compososite likelihood based on all pairwise bivariate models.

Description

The pairwise pairwise odds ratio model provides an alternative to the alternating logistic regression (ALR).

Usage

binomial.twostage(margbin, data = sys.parent(), score.method = "nlminb",
  Nit = 60, detail = 0, clusters = NULL, silent = 1, weights = NULL,
  control = list(), theta = NULL, theta.des = NULL, var.link = 1,
  iid = 1, step = 0.5, notaylor = 1, model = "plackett",
  marginal.p = NULL, strata = NULL, max.clust = NULL,
  se.clusters = NULL, numDeriv = 0)

Arguments

margbin

Marginal binomial model

data

data frame

score.method

Scoring method

Nit

Number of iterations

detail

Detail

clusters

Cluster variable

silent

Debug information

weights

Weights for log-likelihood, can be used for each type of outcome in 2x2 tables.

control

Optimization arguments

theta

Starting values for variance components

theta.des

Variance component design

var.link

Link function for variance

iid

Calculate i.i.d. decomposition

step

Step size

notaylor

Taylor expansion

model

model

marginal.p

vector of marginal probabilities

strata

strata for fitting: considers only pairs where both are from same strata

max.clust

max clusters

se.clusters

clusters for iid decomposition for roubst standard errors

numDeriv

uses Fisher scoring aprox of second derivative if 0, otherwise numerical derivatives

Details

The reported standard errors are based on a cluster corrected score equations from the pairwise likelihoods assuming that the marginals are known. This gives correct standard errors in the case of the Plackett distribution (OR model for dependence), but incorrect standard errors for the Clayton-Oakes types model.

References

Two-stage binomial modelling

Examples

data(twinstut)
twinstut0 <- subset(twinstut, tvparnr<2300000)
twinstut <- twinstut0
theta.des <- model.matrix( ~-1+factor(zyg),data=twinstut)
margbin <- glm(stutter~factor(sex)+age,data=twinstut,family=binomial())
bin <- binomial.twostage(margbin,data=twinstut,
		         clusters=twinstut$tvparnr,theta.des=theta.des,detail=0,
	                 score.method="fisher.scoring")
summary(bin)

twinstut$cage <- scale(twinstut$age)
theta.des <- model.matrix( ~-1+factor(zyg)+cage,data=twinstut)
bina <- binomial.twostage(margbin,data=twinstut,
		         clusters=twinstut$tvparnr,theta.des=theta.des,detail=0,
	                 score.method="fisher.scoring")
summary(bina)

theta.des <- model.matrix( ~-1+factor(zyg)+factor(zyg)*cage,data=twinstut)
bina <- binomial.twostage(margbin,data=twinstut,
		         clusters=twinstut$tvparnr,theta.des=theta.des,detail=0,
	                 score.method="fisher.scoring")
summary(bina)

twinstut$binstut <- (twinstut$stutter=="yes")*1
## refers to zygosity of first subject in eash pair : zyg1
## could also use zyg2 (since zyg2=zyg1 within twinpair's))
out <- easy.binomial.twostage(stutter~factor(sex)+age,data=twinstut,
                          response="binstut",id="tvparnr",
	             	      theta.formula=~-1+factor(zyg1),
                          score.method="fisher.scoring")
summary(out)

## refers to zygosity of first subject in eash pair : zyg1
## could also use zyg2 (since zyg2=zyg1 within twinpair's))
desfs<-function(x,num1="zyg1",num2="zyg2")
    c(x[num1]=="dz",x[num1]=="mz",x[num1]=="os")*1

out3 <- easy.binomial.twostage(binstut~factor(sex)+age,
      data=twinstut,response="binstut",id="tvparnr",
      score.method="fisher.scoring",theta.formula=desfs,desnames=c("mz","dz","os"))
summary(out3)