R Under development (unstable) (2023-12-13 r85679 ucrt) -- "Unsuffered Consequences"
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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$coefficients))]
> parm.start <- c(alpha.start, rep(0, nblk + npop))
> 
> tout <- trust(objfun = penmlogl, parm.start, rinit = 1, rmax = 10,
+     sigma = theta.start, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout)
> tout$converged
[1] TRUE
> 
> ### 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)))
> 
> ### now for the testing
> 
> pout <- penmlogl(tout$argument, theta.crude, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout)
> names(pout)
[1] "value"          "gradient"       "hessian"        "argument"      
[5] "scale"          "mlogl.hessian"  "mlogl.gradient"
> 
> eff.fix <- tout$argument[seq(1, nfix)]
> eff.blk <- tout$argument[seq(nfix + 1, nfix + nblk)]
> eff.pop <- tout$argument[seq(nfix + nblk + 1, nfix + nblk + npop)]
> eff.blk <- theta.crude[1] * eff.blk
> eff.pop <- theta.crude[2] * eff.pop
> 
> beta <- c(eff.fix, eff.blk, eff.pop)
> scalevec <- rep(c(1, theta.crude), times = c(nfix, nblk, npop))
> penaltyvec <- rep(c(0, 1, 1), times = c(nfix, nblk, npop))
> 
> modmat <- cbind(modmat.fix, modmat.blk, modmat.pop)
> mout <- mlogl(beta, pred, fam, aout$x, aout$root, modmat, deriv = 2)
> 
> all.equal(mout$hessian * outer(scalevec, scalevec), pout$mlogl.hessian)
[1] TRUE
> all.equal(tout$argument, pout$argument)
[1] TRUE
> all.equal(scalevec, pout$scale)
[1] TRUE
> 
> all.equal(pout$value,
+     mout$value + sum(penaltyvec * pout$argument^2) / 2)
[1] TRUE
> all.equal(pout$gradient,
+     mout$gradient * scalevec + penaltyvec * pout$argument)
[1] TRUE
> all.equal(pout$hessian, pout$mlogl.hessian + diag(penaltyvec))
[1] TRUE
> 
> epsilon <- 1e-7
> mygradient <- 0 * pout$gradient
> myhessian <- 0 * pout$hessian
> for (i in seq(along = mygradient)) {
+     arg <- tout$argument
+     arg[i] <- arg[i] + epsilon
+     pout.epsilon <- penmlogl(arg, theta.crude, fixed = modmat.fix,
+         random = list(modmat.blk, modmat.pop), obj = aout)
+     mygradient[i] <- (pout.epsilon$value - pout$value) / epsilon
+     myhessian[i, ] <- (pout.epsilon$gradient - pout$gradient) / epsilon
+ }
> all.equal(mygradient, pout$gradient, tol = 100 * epsilon)
[1] TRUE
> all.equal(myhessian, pout$hessian, tol = 100 * epsilon)
[1] TRUE
> 
> ### penmlogl2
> 
> foo <- pout$argument
> alpha2 <- foo[seq(1, nfix)]
> parm2 <- foo[- seq(1, nfix)]
> pout2 <- penmlogl2(parm2, alpha2, theta.crude, fixed = modmat.fix,
+     random = list(modmat.blk, modmat.pop), obj = aout)
> identical(pout$value, pout2$value)
[1] TRUE
> identical(pout$argument, pout2$argument)
[1] TRUE
> identical(pout$scale[- seq(1, nfix)], pout2$scale)
[1] TRUE
> identical(pout$mlogl.hessian, pout2$mlogl.hessian)
[1] TRUE
> identical(pout$mlogl.gradient, pout2$mlogl.gradient)
[1] TRUE
> idx <- seq(nfix + 1, nfix + nblk + npop)
> identical(pout2$gradient, pout$gradient[idx])
[1] TRUE
> foom <- pout$hessian
> foom <- foom[idx, ]
> foom <- foom[ , idx]
> identical(pout2$hessian, foom)
[1] TRUE
> 
> 
> proc.time()
   user  system elapsed 
   0.46    0.10    0.51