group.recover {rare} | R Documentation |
The function finds aggregated groups of leaf indices by traversing non-zero
gamma
elements and finding descendant leaves at each gamma
element. In our problem,
gamma
are latent variables corresponding to tree nodes. The order
of the traversal is post-order, i.e., a node is visited after its descendants.
group.recover(gamma, A, postorder = seq(ncol(A)))
gamma |
Length- |
A |
|
postorder |
Length- |
Returns a list of recovered groups of leaf indices.
## Not run: # See vignette for more details. set.seed(100) ts <- sample(1:length(data.rating), 400) # Train set indices # Fit the model on train set ourfit <- rarefit(y = data.rating[ts], X = data.dtm[ts, ], hc = data.hc, lam.min.ratio = 1e-6, nlam = 20, nalpha = 10, rho = 0.01, eps1 = 1e-5, eps2 = 1e-5, maxite = 1e4) # Cross validation ourfit.cv <- rarefit.cv(ourfit, y = data.rating[ts], X = data.dtm[ts, ], rho = 0.01, eps1 = 1e-5, eps2 = 1e-5, maxite = 1e4) # Group recovered at optimal beta and gamma ibest.lambda <- ourfit.cv$ibest[1] ibest.alpha <- ourfit.cv$ibest[2] gamma.opt <- ourfit$gamma[[ibest.alpha]][, ibest.lambda] # works if ibest.alpha > 1 groups.opt <- group.recover(gamma.opt, ourfit$A) ## End(Not run)