Running 'aster.R' [1s/1s]
  Comparing 'aster.Rout' to 'aster.Rout.save' ... OK
  Running 'bogus.R' [1s/2s]
  Comparing 'bogus.Rout' to 'bogus.Rout.save' ... OK
  Running 'cache.R' [2s/2s]
  Running 'chk.R' [0s/1s]
  Comparing 'chk.Rout' to 'chk.Rout.save' ... OK
  Running 'famber.R' [0s/1s]
  Comparing 'famber.Rout' to 'famber.Rout.save' ... OK
  Running 'famnegbin.R' [0s/1s]
  Comparing 'famnegbin.Rout' to 'famnegbin.Rout.save' ... OK
  Running 'famnorloc.R' [0s/1s]
  Comparing 'famnorloc.Rout' to 'famnorloc.Rout.save' ... OK
  Running 'famnzp.R' [0s/1s]
  Comparing 'famnzp.Rout' to 'famnzp.Rout.save' ... OK
  Running 'fampoi.R' [0s/1s]
  Comparing 'fampoi.Rout' to 'fampoi.Rout.save' ... OK
  Running 'famtnb.R' [0s/1s]
  Comparing 'famtnb.Rout' to 'famtnb.Rout.save' ... OK
  Running 'foobar.R' [0s/1s]
  Comparing 'foobar.Rout' to 'foobar.Rout.save' ... OK
  Running 'formula.R' [0s/1s]
  Comparing 'formula.Rout' to 'formula.Rout.save' ... OK
  Running 'gradmat.R' [1s/1s]
  Comparing 'gradmat.Rout' to 'gradmat.Rout.save' ... OK
  Running 'ktnb.R' [1s/1s]
  Comparing 'ktnb.Rout' to 'ktnb.Rout.save' ... OK
  Running 'ktp.R' [1s/1s]
  Comparing 'ktp.Rout' to 'ktp.Rout.save' ... OK
  Running 'matops.R' [0s/0s]
  Comparing 'matops.Rout' to 'matops.Rout.save' ... OK
  Running 'mlogl-c-export.R' [1s/1s]
  Comparing 'mlogl-c-export.Rout' to 'mlogl-c-export.Rout.save' ... OK
  Running 'mlogl-cond.R' [1s/1s]
  Comparing 'mlogl-cond.Rout' to 'mlogl-cond.Rout.save' ... OK
  Running 'mlogl-unco.R' [1s/1s]
  Comparing 'mlogl-unco.Rout' to 'mlogl-unco.Rout.save' ... OK
  Running 'newfam.R' [0s/1s]
  Comparing 'newfam.Rout' to 'newfam.Rout.save' ... OK
  Running 'newpickle.R' [1s/1s]
  Comparing 'newpickle.Rout' to 'newpickle.Rout.save' ... OK
  Running 'nzp.R' [1s/1s]
  Comparing 'nzp.Rout' to 'nzp.Rout.save' ... OK
  Running 'penmlogl.R' [1s/1s]
  Comparing 'penmlogl.Rout' to 'penmlogl.Rout.save' ... OK
  Running 'pickle.R' [3s/4s]
  Comparing 'pickle.Rout' to 'pickle.Rout.save' ... OK
  Running 'predict.R' [1s/1s]
  Comparing 'predict.Rout' to 'predict.Rout.save' ... OK
  Running 'quickle.R' [4s/4s]
  Comparing 'quickle.Rout' to 'quickle.Rout.save' ... OK
  Running 'raster.R' [0s/0s]
  Comparing 'raster.Rout' to 'raster.Rout.save' ... OK
  Running 'reaster-warn.R' [2s/2s]
  Comparing 'reaster-warn.Rout' to 'reaster-warn.Rout.save' ... OK
  Running 'reaster.R' [8s/8s]
  Comparing 'reaster.Rout' to 'reaster.Rout.save' ... OK
  Running 'reaster1.R' [1s/1s]
  Comparing 'reaster1.Rout' to 'reaster1.Rout.save' ... OK
  Running 'trans.R' [0s/1s]
  Comparing 'trans.Rout' to 'trans.Rout.save' ... OK
Running the tests in 'tests/cache.R' failed.
Complete output:
  > 
  > library(aster)
  Loading required package: 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)
  + )
  Killed