binomial.twostage: Fits Clayton-Oakes or bivariate Plackett (OR) models for...

In mets: Analysis of Multivariate Event Times

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.

Author(s)

Thomas Scheike

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)

mets documentation built on May 2, 2019, 4:43 p.m.