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ram_calc = function(par,SampCov22,A,S,F,SampMean){
#mult = extractMatrices(fit.growth)
#A <- mult$A
# S <- mult$S
# F <- mult$F
ret <- list()
#coefs = coef(fit.growth)
# par = c(coefs[1:2],coefs[10:11],coefs[3:9])
A2 <- A
S2 <- S
# doesn't work for
for(i in 1:length(par)){
A2[A2== i] <- par[i]
S2[S2== i] <- par[i]
}
#A2[A.fixed] <- A.est[A.fixed]
#S2[S.fixed] <- S.est[S.fixed]
#ImpCov = F %*% solve(I-A2) %*% S2 %*% t(solve(I-A2)) %*% t(F)
nncol = which(colnames(A2) == "1")
m = A2[-nncol,"1"]
m.pars = A[-nncol,"1"]
#m.pars = A[-nncol,"1"]
A.pars = A[-nncol,-nncol]
A2 = A2[-nncol,-nncol]
F= F[-nncol,-nncol]
dd = SampMean
S2 = S2[-nncol,-nncol]
S.pars = S[-nncol,-nncol]
I = diag(nrow(A2))
mu = F %*% solve(I - A2) %*% m
#I = diag(nrow(Areg))
ImpCov = F %*% solve(I-A2) %*% S2 %*% t(solve(I-A2)) %*% t(F)
#grad.out <- rep(0,length(par))
B = solve(I - A2)
C = diag(nrow(ImpCov)) - (solve(ImpCov) %*% SampCov22)
E = B %*% S2 %*% t(B)
b = dd - mu
lik = log(((2*pi)^length(b)) * det(ImpCov)) + trace(solve(ImpCov) %*% SampCov22) + t(b) %*% solve(ImpCov) %*% b
#lik= log(det(ImpCov)) + trace(SampCov22 %*% solve(ImpCov)) - log(det(SampCov22)) - 4
#A.iter <- max(A)
ret$lik <- lik
ret$ImpCov <- ImpCov
ret$S2 <- S2
ret$A2 <- A2
ret$m <- m
ret$m.pars <- m.pars
ret$A.pars <- A.pars
ret$S.pars <- S.pars
ret$F <- F
ret$mu <- mu
#lik;ImpCov;S2;A2
ret
}
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