R Under development (unstable) (2023-12-13 r85679 ucrt) -- "Unsuffered Consequences"
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> 
>  library(aster)
> 
>  data(echinacea)
> 
>  # cut and paste from help(predict.aster)
>  vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
+      "hdct02", "hdct03", "hdct04")
>  redata <- reshape(echinacea, varying = list(vars), direction = "long",
+      timevar = "varb", times = as.factor(vars), v.names = "resp")
>  redata <- data.frame(redata, root = 1)
>  pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
>  fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
>  hdct <- grepl("hdct", as.character(redata$varb))
>  redata <- data.frame(redata, hdct = as.integer(hdct))
>  level <- gsub("[0-9]", "", as.character(redata$varb))
>  redata <- data.frame(redata, level = as.factor(level))
>  aout <- aster(resp ~ varb + level : (nsloc + ewloc) + hdct : pop,
+      pred, fam, varb, id, root, data = redata)
>  # end of cut and paste from help(predict.aster)
> 
>  nind <- length(unique(redata$id))
>  nnode <- length(pred)
>  ncoef <- length(aout$coefficients)
> 
>  nind
[1] 570
>  nnode
[1] 9
>  ncoef
[1] 21
> 
>  mu <- predict(aout)
>  mu <- matrix(mu, nrow = nind, ncol = nnode)
> 
>  my.xi <- matrix(NaN, nrow = nind, ncol = nnode)
>  for (j in 1:nnode)
+      if (pred[j] == 0) {
+          my.xi[ , j] <- mu[ , j]
+      } else {
+          my.xi[ , j] <- mu[ , j] / mu[ , pred[j]]
+      }
> 
>  old.xi <- predict(aout, model.type = "conditional")
>  old.xi <- matrix(old.xi, nrow = nind, ncol = nnode)
> 
>  max(abs(my.xi - old.xi)) > 1.0
[1] TRUE
> 
>  # force use of method predict.aster rather than predict.aster.formula
>  x.bogo <- matrix(1.0, nrow = nind, ncol = nnode)
>  tricky.xi <- predict.aster(aout, x.bogo, x.bogo, aout$modmat,
+      model.type = "conditional")
>  tricky.xi <- matrix(tricky.xi, nrow = nind, ncol = nnode)
>  all.equal(tricky.xi, my.xi)
[1] TRUE
> 
>  ### now use new feature !!!
>  new.xi <- predict(aout, model.type = "conditional", is.always = TRUE)
>  new.xi <- matrix(new.xi, nrow = nind, ncol = nnode)
>  all.equal(new.xi, my.xi)
[1] TRUE
> 
>  # check gradient
> 
>  xpred <- old.xi / new.xi
>  
>  my.xpred <- matrix(NaN, nrow = nind, ncol = nnode)
>  for (j in 1:nnode)
+      if (pred[j] == 0) {
+          my.xpred[ , j] <- 1
+      } else {
+          my.xpred[ , j] <- aout$x[ , pred[j]]
+      }
> 
>  all.equal(xpred, my.xpred)
[1] TRUE
> 
>  pout.new <- predict(aout, model.type = "conditional", gradient = TRUE,
+      is.always = TRUE)
>  pout.old <- predict(aout, model.type = "conditional", gradient = TRUE)
> 
>  dim(pout.new$gradient)
[1] 5130   21
>  dim(pout.old$gradient)
[1] 5130   21
>  length(xpred)
[1] 5130
> 
>  my.gradient.old <- sweep(pout.new$gradient, 1, as.vector(xpred), "*")
>  all.equal(my.gradient.old, pout.old$gradient)
[1] TRUE
> 
>  ##### check conditional aster model #####
> 
>  aout <- aster(resp ~ varb + level : (nsloc + ewloc + pop),
+      pred, fam, varb, id, root, data = redata, type = "conditional")
> 
>  old.xi <- predict(aout, model.type = "conditional")
>  old.xi <- matrix(old.xi, nrow = nind, ncol = nnode)
> 
>  new.xi <- predict(aout, model.type = "conditional", is.always = TRUE)
>  new.xi <- matrix(new.xi, nrow = nind, ncol = nnode)
>  all.equal(xpred * new.xi, old.xi)
[1] TRUE
> 
>  pout.new <- predict(aout, model.type = "conditional", gradient = TRUE,
+      is.always = TRUE)
>  pout.old <- predict(aout, model.type = "conditional", gradient = TRUE)
> 
>  my.gradient.old <- sweep(pout.new$gradient, 1, as.vector(xpred), "*")
>  all.equal(my.gradient.old, pout.old$gradient)
[1] TRUE
> 
> 
> proc.time()
   user  system elapsed 
   0.59    0.09    0.67