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> 
> library(aster)
> library(trust)
> 
> data(radish)
> 
> pred <- c(0,1,2)
> fam <- c(1,3,2)
> 
> ### need object of type aster to supply to penmlogl and pickle
> 
> aout <- aster(resp ~ varb + fit : (Site * Region + Block + Pop),
+     pred, fam, varb, id, root, data = radish)
> 
> ### model matrices for fixed and random effects
> 
> modmat.fix <- model.matrix(resp ~ varb + fit : (Site * Region),
+     data = radish)
> modmat.blk <- model.matrix(resp ~ 0 + fit:Block, data = radish)
> modmat.pop <- model.matrix(resp ~ 0 + fit:Pop, data = radish)
> 
> rownames(modmat.fix) <- NULL
> rownames(modmat.blk) <- NULL
> rownames(modmat.pop) <- NULL
> 
> idrop <- match(aout$dropped, colnames(modmat.fix))
> idrop <- idrop[! is.na(idrop)]
> modmat.fix <- modmat.fix[ , - idrop]
> 
> nfix <- ncol(modmat.fix)
> nblk <- ncol(modmat.blk)
> npop <- ncol(modmat.pop)
> 
> ### try penmlogl
> 
> theta.start <- c(1, 1)
> 
> alpha.start <- aout$coefficients[match(colnames(modmat.fix),
+     names(aout$coefficients))]
> parm.start <- c(alpha.start, rep(0, nblk + npop))
> 
> tout <- trust(objfun = penmlogl, parm.start, rinit = 1, rmax = 10,
+     sigma = theta.start, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout)
> 
> ### crude estimate of variance components
> 
> eff.blk <- tout$argument[seq(nfix + 1, nfix + nblk)]
> eff.pop <- tout$argument[seq(nfix + nblk + 1, nfix + nblk + npop)]
> theta.crude <- sqrt(c(var(eff.blk), var(eff.pop)))
> 
> ### try optim and pickle
> 
> lower <- rep(0, length(theta.crude))
> time1 <- system.time(
+ oout <- optim(theta.crude, pickle,
+     parm = tout$argument, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout)
+ )
> 
> time2 <- system.time(
+ oout.cache <- optim(theta.crude, pickle,
+     parm = tout$argument, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout,
+     cache = new.env())
+ )
> 
> all.equal(oout$par, oout.cache$par, tol = 1e-5)
[1] TRUE
> time2[1] < time1[1]
user.self 
     TRUE 
> 
> 
> proc.time()
   user  system elapsed 
  1.861   0.020   1.873