# 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)
# Prediction on test set
pred <- rarefit.predict(ourfit, ourfit.cv, data.dtm[-ts, ])
pred.error <- mean((pred - data.rating[-ts])^2)
# }
# NOT RUN {
# }
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