mc.est.CMData {CorrBin}

R Documentation

Distribution of the number of responses assuming marginal compatibility.

Description

The mc.est function estimates the distribution of the number of responses in a cluster under the assumption of marginal compatibility: information from all cluster sizes is pooled. The estimation is performed independently for each treatment group.

Usage

## S3 method for class 'CMData'
mc.est(object, eps = 1e-06, ...)

## S3 method for class 'CBData'
mc.est(object, ...)

mc.est(object, ...)

Arguments

object	a CBData or CMData object
eps	numeric; EM iterations proceed until the sum of squared changes fall below eps
...	other potential arguments; not currently used

Details

The EM algorithm given by Stefanescu and Turnbull (2003) is used for the binary data.

Value

For CMData: A data frame giving the estimated pdf for each treatment and clustersize. The probabilities add up to 1 for each Trt/ClusterSize combination. It has the following columns:

Prob	numeric, the probability of NResp responses in a cluster of size ClusterSize in group Trt
Trt	factor, the treatment group
ClusterSize	numeric, the cluster size
NResp.1 - NResp.K	numeric, the number of responses of each type

For CBData: A data frame giving the estimated pdf for each treatment and clustersize. The probabilities add up to 1 for each Trt/ClusterSize combination. It has the following columns:

Prob	numeric, the probability of NResp responses in a cluster of size ClusterSize in group Trt
Trt	factor, the treatment group
ClusterSize	numeric, the cluster size
NResp	numeric, the number of responses

Note

For multinomial data, the implementation is curerntly written in R, so it is not very fast.

Author(s)

Aniko Szabo

References

Stefanescu, C. & Turnbull, B. W. (2003) Likelihood inference for exchangeable binary data with varying cluster sizes. Biometrics, 59, 18-24

Examples

data(dehp)
dehp.mc <- mc.est(subset(dehp, Trt=="0"))
subset(dehp.mc, ClusterSize==2)

data(shelltox)
sh.mc <- mc.est(shelltox)

library(lattice)
xyplot(Prob~NResp|factor(ClusterSize), groups=Trt, data=sh.mc, subset=ClusterSize>0, 
   type="l", as.table=TRUE, auto.key=list(columns=4, lines=TRUE, points=FALSE),
   xlab="Number of responses", ylab="Probability P(R=r|N=n)")

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