ForwardModel.Bin: IDI/NRI-based feature selection procedure for linear,...

In FRESA.CAD: Feature Selection Algorithms for Computer Aided Diagnosis

IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regression models

Description

This function performs a bootstrap sampling to rank the variables that statistically improve prediction. After the frequency rank, the function uses a forward selection procedure to create a final model, whose terms all have a significant contribution to the integrated discrimination improvement (IDI) or the net reclassification improvement (NRI). For each bootstrap, the IDI/NRI is computed and the variable with the largest statically significant IDI/NRI is added to the model. The procedure is repeated at each bootstrap until no more variables can be inserted. The variables that enter the model are then counted, and the same procedure is repeated for the rest of the bootstrap loops. The frequency of variable-inclusion in the model is returned as well as a model that uses the frequency of inclusion.

Usage

	ForwardSelection.Model.Bin(size = 100,
	                            fraction = 1,
	                            pvalue = 0.05, 
	                            loops = 100,
	                            covariates = "1",
	                            Outcome,
	                            variableList,
	                            data, 
	                            maxTrainModelSize = 20,
	                            type = c("LM", "LOGIT", "COX"),
	                            timeOutcome = "Time",
	                            selectionType=c("zIDI", "zNRI"),
	                            cores = 6,
	                            randsize = 0,
	                            featureSize=0)

Arguments

size	The number of candidate variables to be tested (the first size variables from variableList)
fraction	The fraction of data (sampled with replacement) to be used as train
pvalue	The maximum p-value, associated to either IDI or NRI, allowed for a term in the model
loops	The number of bootstrap loops
covariates	A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates)
Outcome	The name of the column in data that stores the variable to be predicted by the model
variableList	A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables
data	A data frame where all variables are stored in different columns
maxTrainModelSize	Maximum number of terms that can be included in the model
type	Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
timeOutcome	The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)
selectionType	The type of index to be evaluated by the improveProb function (Hmisc package): z-score of IDI or of NRI
cores	Cores to be used for parallel processing
randsize	the model size of a random outcome. If randsize is less than zero. It will estimate the size
featureSize	The original number of features to be explored in the data frame.

Value

final.model	An object of class lm, glm, or coxph containing the final model
var.names	A vector with the names of the features that were included in the final model
formula	An object of class formula with the formula used to fit the final model
ranked.var	An array with the ranked frequencies of the features
z.selection	A vector in which each term represents the z-score of the index defined in selectionType obtained with the Full model and the model without one term
formula.list	A list containing objects of class formula with the formulas used to fit the models found at each cycle
variableList	A list of variables used in the forward selection

Author(s)

Jose G. Tamez-Pena and Antonio Martinez-Torteya

References

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.

See Also

ForwardSelection.Model.Res

FRESA.CAD documentation built on Nov. 25, 2023, 1:07 a.m.