Nothing
covLCA.covLAlphaGamma <-
function(J,S2,K.j,R,z,y,probs,N,rgivy) #A: Why the difference between the 2 possible computations ?
{
Cov=matrix(nrow=J*S2*(K.j[1]-1),ncol=J*R*(K.j[1]-1)) #Does not need to be adapted
ind1=0
for (i1 in 1:J) #A: for each manifest variable m
{
for (i2 in 1:S2) #A: for each covariate q
{
for (i3 in 1:(K.j[1]-1)) #A: for each category k
{
ind1=ind1+1
ind2=0
for (i4 in 1:J) #A: for each manifest variable u
{
for (i5 in 1:R) #A: for each latent class l
{
for (i6 in 1:(K.j[1]-1)) #A: for each category s
{
ind2=ind2+1
Cov[ind1,ind2]= - ((z[,i2]*((y[,i4]==i6)-probs[,i4,i6,i5]))%*%apply((probs[,i1,i3,]*rgivy*(matrix(rep(((1:R)==i5),rep(N,R)),nrow=N,ncol=R)-matrix(rep(rgivy[,i5],R),nrow=N,ncol=R))),1,sum))
}
}
}
}
}
}
return(Cov)
}
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