bsnullinteract: Compute bootstrapped null interaction prediction rule...

In pre: Prediction Rule Ensembles

Compute bootstrapped null interaction prediction rule ensembles

Description

bsnullinteract generates bootstrapped null interaction models, which can be used to derive a reference distribution of the test statistic calculated with interact.

Usage

bsnullinteract(
  object,
  nsamp = 10,
  parallel = FALSE,
  penalty.par.val = "lambda.1se",
  verbose = FALSE,
  ...
)

Arguments

object	object of class pre.
nsamp	numeric. Number of bootstrapped null interaction models to be derived.
parallel	logical. Should parallel foreach be used to generate initial ensemble? Must register parallel beforehand, such as doMC or others.
penalty.par.val	character or numeric. Value of the penalty parameter \lambda to be employed for selecting the final ensemble. The default "lambda.min" employs the \lambda value within 1 standard error of the minimum cross-validated error. Alternatively, "lambda.min" may be specified, to employ the \lambda value with minimum cross-validated error, or a numeric value >0 may be specified, with higher values yielding a sparser ensemble. To evaluate the trade-off between accuracy and sparsity of the final ensemble, inspect pre_object$glmnet.fit and plot(pre_object$glmnet.fit).
verbose	logical. should progress be printed to the command line?
...	Further arguments to be passed to predict.pre.

Details

Note that computation of bootstrapped null interaction models is computationally intensive. The default number of samples is set to 10, but for reliable results argument nsamp should be set to a higher value (e.g., \ge 100).

See also section 8.3 of Friedman & Popescu (2008).

Value

A list of length nsamp with null interaction models, to be used as input for interact.

References

Fokkema, M. (2020). Fitting prediction rule ensembles with R package pre. Journal of Statistical Software, 92(12), 1-30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v092.i12")}

Friedman, J. H., & Popescu, B. E. (2008). Predictive learning via rule ensembles. The Annals of Applied Statistics, 2(3), 916-954, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/07-AOAS148")}.

See Also

pre, interact

Examples

set.seed(42)
airq.ens <- pre(Ozone ~ ., data=airquality[complete.cases(airquality),])
nullmods <- bsnullinteract(airq.ens)
interact(airq.ens, nullmods = nullmods, col = c("#7FBFF5", "#8CC876"))

pre documentation built on May 29, 2024, 5:10 a.m.