gpe  R Documentation 
Provides an interface for deriving sparse prediction ensembles where basis functions are selected through L1 penalization.
gpe( formula, data, base_learners = list(gpe_trees(), gpe_linear()), weights = rep(1, times = nrow(data)), sample_func = gpe_sample(), verbose = FALSE, penalized_trainer = gpe_cv.glmnet(), model = TRUE )
formula 
Symbolic description of the model to be fit of the form

data 

base_learners 
List of functions which has formal arguments

weights 
Case weights with length equal to number of rows in 
sample_func 
Function used to sample when learning with base learners.
The function should have formal argument 
verbose 

penalized_trainer 
Function with formal arguments 
model 

Provides a more general framework for making a sparse prediction ensemble than
pre
.
By default, a similar fit to pre
is obtained. In addition,
multivariate adaptive regression splines (Friedman, 1991) can be included
with gpe_earth
. See examples.
Other customs base learners can be implemented. See gpe_trees
,
gpe_linear
or gpe_earth
for details of the setup.
The sampling function given by sample_func
can also be replaced by a
custom sampling function. See gpe_sample
for details of the setup.
An object of class gpe
.
Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916954. Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 167.
pre
, gpe_trees
,
gpe_linear
, gpe_earth
,
gpe_sample
, gpe_cv.glmnet
## Not run: ## Obtain similar fit to function pre: gpe.rules < gpe(Ozone ~ ., data = airquality[complete.cases(airquality),], base_learners = list(gpe_linear(), gpe_trees())) gpe.rules ## Also include products of hinge functions using MARS: gpe.hinge < gpe(Ozone ~ ., data = airquality[complete.cases(airquality),], base_learners = list(gpe_linear(), gpe_trees(), gpe_earth())) ## End(Not run)
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