This function removes model terms that do not improve the bootstrapped integrated discrimination improvement (IDI) or net reclassification improvement (NRI) significantly.
bootstrapVarElimination_Bin(object,
pvalue = 0.05,
Outcome = "Class",
data,
startOffset = 0,
type = c("LOGIT", "LM", "COX"),
selectionType = c("zIDI", "zNRI"),
loops = 64,
print=TRUE,
plots=TRUE
)An object of class lm, glm, or coxph containing the model to be analyzed
The maximum p-value, associated to either IDI or NRI, allowed for a term in the model
The name of the column in data that stores the variable to be predicted by the model
A data frame where all variables are stored in different columns
Only terms whose position in the model is larger than the startOffset are candidates to be removed
Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
The type of index to be evaluated by the improveProb function (Hmisc package): z-score of IDI or of NRI
The number of bootstrap loops
Logical. If TRUE, information will be displayed
Logical. If TRUE, plots are displayed
An object of the same class as object containing the reduced model
The number of loops it took for the model to stabilize
A list with the NRI and IDI statistics of the reduced model, as given by the getVar.Bin function
An object of class bootstrapValidation_Bin containing the results of the bootstrap validation in the reduced model
An object of class formula with the formula used to fit the reduced model
The name of the last term that was removed (-1 if all terms were removed)
The model will have the fitted model that had close to maximum bootstrapped test accuracy
The formula of the model before False Selection Correction
the string formula of the model that had the best or close to tbe best test accuracy
For each model term \(x_i\), the IDI or NRI is computed for the Full model and the reduced model( where the term \(x_i\) removed). The term whose removal results in the smallest drop in bootstrapped improvement is selected. The hypothesis: the term adds classification improvement is tested by checking the p value of average improvement. If \(p(IDI or NRI)>pvalue\), then the term is removed. In other words, only model terms that significantly aid in subject classification are kept. The procedure is repeated until no term fulfils the removal criterion.
Pencina, M. J., D'Agostino, R. B., & Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine 27(2), 157-172.
bootstrapVarElimination_Res,
backVarElimination_Bin,
backVarElimination_Res