prune_pre {pre}

Get the optimal lambda and gamma parameter values for an ensemble of given size

Description

Function prune_pre returns the optimal values of lambda and gamma for the requested ensemble size.

Usage

prune_pre(object, nonzero, plusminus = 3)

Arguments

object

an object of class pre that was fit using the relaxed lasso. If an object of class pre is specified that was not fit using the relaxed lasso, an error will be printed.

nonzero

maximum number of terms to retain.

plusminus

number of terms above and below nonzero for which CV results will be printed.

Value

The lambda and gamma values that yield optimal predictive accuracy for the specified number of terms. These are invisibly returned, see Examples on how to use them. A sentence describing what the optimal values are is printed to the command line, with an overview of the performance (in terms of cross-validated accuracy and the number of terms retained) of lambda values near the optimum. If the specified number of terms to retain is lower than what would be obtained using the lambda.min or lambda.1se criterion, a warning will also be printed.

See Also

pre

Examples

## Fit a rule ensemble to predict Ozone concentration
airq <- airquality[complete.cases(airquality), ]
set.seed(42)
airq.ens <- pre(Ozone ~ ., data = airq, relax = TRUE)

## Inspect the result (default lambda.1se criterion)
airq.ens

## Inspect the lambda path 
## (lower x-axis gives lambda values, upper x-axis corresponding no. of non-zero terms)
## Not run: plot(airq.ens$glmnet.fit)

## Accuracy still quite good with only 5 terms, obtain corresponding parameter values
opt_pars <- prune_pre(airq.ens, nonzero = 5)
opt_pars

## Use the parameter values for interpretation and prediction, e.g.
predict(airq.ens, newdat = airq[c(22, 33), ], penalty = opt_pars$lambda, gamma = opt_pars$gamma)
summary(airq.ens, penalty = opt_pars$lambda, gamma = opt_pars$gamma)
print(airq.ens, penalty = opt_pars$lambda, gamma = opt_pars$gamma)