loglik1 | R Documentation |
Compute the log likelihood of a probability mass function, without assuming MCAR, given complete and incomplete data
loglik1(p0, pS, p0h, n0, pSh, nS, bS, M)
p0 |
A probability mass function on the joint space. |
pS |
A sequence of probability mass functions on the marginal spaces. |
p0h |
An empirical mass function calculated using complete observations. |
n0 |
An integer giving the number of complete observations used to calculate |
pSh |
A sequence of empirical mass functions calculated using incomplete observations. |
nS |
A sequence of integers giving the numbers of incomplete observations used to calculate |
bS |
A binary matrix specifying the set of observation patterns. Each row encodes a single pattern. |
M |
A vector of positive integers giving the alphabet sizes of the discrete variables. |
The value of the log likelihood.
bS=matrix(c(1,1,0, 1,0,1, 0,1,1),byrow=TRUE,ncol=3) # Our canonical 3d example
M=c(2,2,2)
n0=200
nS=c(200,200,200)
pS=c(0.125,0.375,0.375,0.125,0.250,0.250,0.250,0.250,0.100,0.400,0.400,0.100)
P12=pS[1:4]; P13=pS[5:8]; P23=pS[9:12]
X12=t(rmultinom(1,size=nS[1],prob=P12)/nS[1])
X13=t(rmultinom(1,size=nS[2],prob=P13)/nS[2])
X23=t(rmultinom(1,size=nS[3],prob=P23)/nS[3])
pSh=cbind(X12,X13,X23)
p0=array(0.125,dim=c(2,2,2))
p0h=array(rmultinom(1,n0,p0),dim=M)/n0
loglik1(p0,pS,p0h,n0,pSh,nS,bS,M)
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