abe.boot: Bootstrapped Augmented Backward Elimination

In abe: Augmented Backward Elimination

Bootstrapped Augmented Backward Elimination

Description

'r lifecycle::badge("deprecated")'

This function was deprecated, use 'abe.resampling' instead.

Performs Augmented backward elimination on re-sampled datasets using different bootstrap and re-sampling techniques.

Usage

abe.boot(
  fit,
  data = NULL,
  include = NULL,
  active = NULL,
  tau = 0.05,
  exp.beta = TRUE,
  exact = FALSE,
  criterion = "alpha",
  alpha = 0.2,
  type.test = "Chisq",
  type.factor = NULL,
  num.boot = 100,
  type.boot = c("bootstrap", "mn.bootstrap", "subsampling"),
  prop.sampling = 0.5
)

Arguments

fit	An object of a class '"lm"', '"glm"' or '"coxph"' representing the fit. Note, the functions should be fitted with argument 'x=TRUE' and 'y=TRUE'.
data	data frame used when fitting the object 'fit'.
include	a vector containing the names of variables that will be included in the final model. These variables are used as passive variables during modeling. These variables might be exposure variables of interest or known confounders. They will never be dropped from the working model in the selection process, but they will be used passively in evaluating change-in-estimate criteria of other variables. Note, variables which are not specified as include or active in the model fit are assumed to be active and passive variables.
active	a vector containing the names of active variables. These less important explanatory variables will only be used as active, but not as passive variables when evaluating the change-in-estimate criterion.
tau	Value that specifies the threshold of the relative change-in-estimate criterion. Default is set to 0.05.
exp.beta	Logical specifying if exponent is used in formula to standardize the criterion. Default is set to TRUE.
exact	Logical, specifies if the method will use exact change-in-estimate or approximated. Default is set to FALSE, which means that the method will use approximation proposed by Dunkler et al. Note, setting to TRUE can severely slow down the algorithm, but setting to FALSE can in some cases lead to a poor approximation of the change-in-estimate criterion.
criterion	String that specifies the strategy to select variables for the blacklist. Currently supported options are significance level ''alpha'‘, Akaike information criterion '’AIC'‘ and Bayesian information criterion '’BIC''. If you are using significance level, in that case you have to specify the value of 'alpha' (see parameter 'alpha'). Default is set to '"alpha"'.
alpha	Value that specifies the level of significance as explained above. Default is set to 0.2.
type.test	String that specifies which test should be performed in case the 'criterion = "alpha"'. Possible values are '"F"' and '"Chisq"' (default) for class '"lm"', '"Rao"', '"LRT"', '"Chisq"' (default), '"F"' for class '"glm"' and '"Chisq"' for class '"coxph"'. See also [drop1()].
type.factor	String that specifies how to treat factors, see details, possible values are '"factor"' and '"individual"'.
num.boot	number of bootstrap re-samples
type.boot	String that specifies the type of bootstrap. Possible values are '"bootstrap"', '"mn.bootstrap"', '"subsampling"', see details
prop.sampling	Sampling proportion. Only applicable for 'type.boot="mn.bootstrap"' and 'type.boot="subsampling"', defaults to 0.5. See details.

Details

Used only for compatibility with the previous versions and will be removed at some point; see/use [abe.resampling()] instead.

Value

an object of class 'abe' for which 'summary', 'plot' and 'pie.abe' functions are available. A list with the following elements:

'models' the final models obtained after performing ABE on re-sampled datasets, each object in the list is of the same class as 'fit'

'alpha' the vector of significance levels used

'tau' the vector of threshold values for the change-in-estimate

'num.boot' number of re-sampled datasets

'criterion' criterion used when constructing the black-list

'all.vars' a list of variables used when estimating 'fit'

'fit.or' the initial model

'misc' the parameters of the call to 'abe.boot'

Author(s)

Rok Blagus, [e-mail]

Daniela Dunkler

Sladana Babic

References

Daniela Dunkler, Max Plischke, Karen Lefondre, and Georg Heinze. Augmented backward elimination: a pragmatic and purposeful way to develop statistical models. PloS one, 9(11):e113677, 2014.

Riccardo De Bin, Silke Janitza, Willi Sauerbrei and Anne-Laure Boulesteix. Subsampling versus Bootstrapping in Resampling-Based Model Selection for Multivariable Regression. Biometrics 72, 272-280, 2016.

See Also

abe.resampling

abe documentation built on April 12, 2025, 1:54 a.m.