get_what.lasso_screenr: An S3 Method for Extraction of Components from...

In sgutreuter/screenr: Construction of Binary Test-Screening Rules

An S3 Method for Extraction of Components from lasso_screenr Objects

Description

get_what.lasso_screenr extracts components from lasso_screenr-class objects.

Usage

## S3 method for class 'lasso_screenr'
get_what(
  from = NULL,
  what = c("glmpathObj", "ROCci", "cvROC", "isROC"),
  ...,
  model = c("minAIC", "minBIC"),
  conf_level = 0.95,
  bootreps = 4000,
  se_min = 0.8
)

Arguments

from	the lasso_screenr-class object from which to extract the component.
what	the character-valued name of the component to extract. Valid values are ⁠"glmpathObj"⁠, ⁠"ROCci"⁠, ⁠"cvROC"⁠ and ⁠"isROC"⁠.
...	optional arguments to get_what methods.
model	the character-valued name of the model for which the component is desired. Valid values are ⁠"minAIC"⁠ and ⁠"minBIC"⁠. Default: ⁠"minAIC"⁠.
conf_level	confidence level for what = ⁠"ROCci"⁠. Default: 0.95.
bootreps	the number of bootstrap replications for estimation of confidence intervals for what = ⁠"ROCci"⁠. Default: 4000.
se_min	minimum value of sensitivity printed for what = ⁠ROCci⁠. Default: 0.8.

Details

get_what is provided to enable easy extraction of components that are not provided by the coef, plot, predict, print or summary methods.

The following values of what return:

⁠"glmpathObj"⁠

the entire glmpath-class object produced by by glmpath

.

⁠ROCci⁠

a data frame containing cross-validated sensitivities, specificities and their confidence limits, and thresholds

.

⁠"cvROC"⁠

the roc-class object produced by roc containing the k-fold cross-validated receiver-operating characteristic.

⁠"isROC"⁠

the roc-class object produced by roc containing the in-sample (overly optimistic) receiver-operating characteristic.

Value

get_what.lasso_screenr returns (invisibly) the object specified by what.

Examples

## Not run: 
attach(uniobj1)
## Plot the coefficient paths
pathobj <- get_what(from = uniobj1, what = "glmpathObj", model = "minAIC")
plot(pathobj, xvar = "lambda")
## Get and print cross-validated sensitivities and specificities at
##   thresholds for the local maxima of the ROC curve
cvROCci <- get_what(from = uniobj1,  what = "ROCci", model = "minBIC")
print(cvROCci)

## End(Not run)

sgutreuter/screenr documentation built on Oct. 19, 2024, 12:49 p.m.