Description Usage Arguments Value See Also Examples
View source: R/ExchMultinomial.R
An exchangeable multinomial distribution with K+1 categories O_1,…,O_{K+1}, can be parameterized by the joint probabilities of events
tau_{r_1,..,r_K|n} = P[X_1=...=X_{r_1}=O_1,..., X_{sum_{i=1}^{K-1}r_i+1} =...=X_{sum_{i=1}^{K}r_i}=O_K]
where r_i ≥q 0 and r_1+\cdots +r_K≤q n.
The jointprobs
function estimates these probabilities under various settings.
Note that when some of the r_i's equal zero, then no restriction on the number of outcomes of the
corresponding type are imposed, so the resulting probabilities are marginal.
1 | jointprobs(cmdata, type = c("averaged", "cluster", "mc"))
|
cmdata |
a |
type |
character string describing the desired type of estimate:
|
a list with an array of estimates for each treatment. For a multinomial distribution with
K+1 categories the arrays will have either K+1 or K dimensions, depending on whether
cluster-size specific estimates (type="cluster"
) or pooled estimates
(type="averaged"
or type="mc"
) are requested. For the cluster-size specific estimates
the first dimension is the cluster-size. Each additional dimension is a possible outcome.
mc.est
for estimating the distribution under marginal compatibility,
uniprobs
and multi.corr
for extracting the univariate marginal event
probabilities, and the within-multinomial correlations from the joint probabilities.
1 2 3 4 5 6 7 8 9 10 | data(dehp)
# averaged over cluster-sizes
tau.ave <- jointprobs(dehp, type="ave")
# averaged P(X1=X2=O1, X3=O2) in the 1500 dose group
tau.ave[["1500"]]["2","1"] # there are two type-1, and one type-2 outcome
#plot P(X1=O1) - the marginal probability of a type-1 event over cluster-sizes
tau <- jointprobs(dehp, type="cluster")
ests <- as.data.frame(lapply(tau, function(x)x[,"1","0"]))
matplot(ests, type="b")
|
[1] 0.03172957
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