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 = 1e06, ...)
## S3 method for class 'CBData'
mc.est(object, ...)
mc.est(object, ...)
Arguments
object 

eps 
numeric; EM iterations proceed until the sum of squared changes fall below 
... 
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 
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 
Trt 
factor, the treatment group 
ClusterSize 
numeric, the cluster size 
NResp 
numeric, the number of responses 
Note
For multinomial data, the implementation is currently written in R, so it is not very fast.
Author(s)
Aniko Szabo
References
George EO, Cheon K, Yuan Y, Szabo A (2016) On Exchangeable Multinomial Distributions. #'Biometrika 103(2), 397408.
Stefanescu, C. & Turnbull, B. W. (2003) Likelihood inference for exchangeable binary data with varying cluster sizes. Biometrics, 59, 1824
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~NRespfactor(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=rN=n)")