rarefit.predict: Make predictions from a rarefit object and a rarefit.cv...

In rare: Linear Model with Tree-Based Lasso Regularization for Rare Features

Description

The function makes predictions using a rarefit object at optimal (lambda, alpha) chosen by rarefit.cv.

Usage

rarefit.predict(fitObj, cvObj, newx)

Arguments

fitObj	Output of rarefit.
cvObj	Output of rarefit.cv.
newx	Matrix of new values for x at which predictions are made.

Value

Returns a sequence of predictions.

See Also

rarefit, rarefit.cv

Examples

## 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)

## End(Not run)

rare documentation built on May 1, 2019, 9:17 p.m.