baggedModel {FRESA.CAD}

Get the bagged model from a list of models

Description

This function will take the frequency-ranked of variables and the list of models to create a single bagged model

Usage

	baggedModel(modelFormulas,
	            data,
	            type=c("LM","LOGIT","COX"),
	            Outcome=NULL,
	            timeOutcome=NULL,
	            frequencyThreshold=0.025,
	            univariate=NULL,
				useFreq=TRUE,
				n_bootstrap=1,
				equifreqCorrection=0
	            )
	baggedModelS(modelFormulas,
                 data,
                 type=c("LM","LOGIT","COX"),
                 Outcome=NULL,
                 timeOutcome=NULL)

Arguments

modelFormulas

The name of the column in data that stores the variable to be predicted by the model

data

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

type

Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")

Outcome

The name of the column in data that stores the time to outcome

timeOutcome

The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)

frequencyThreshold

set the frequency the threshold of the frequency of features to be included in the model)

univariate

The FFRESA.CAD univariate analysis matrix

useFreq

Use the feature frequency to order the formula terms. If set to a positive value is the number of estimation loops

n_bootstrap

if greater than 1, defines the number of bootstraps samples to be used

equifreqCorrection

Indicates the average size of repeated features in an equivalent model

Value

bagged.model

the bagged model

formula

the formula of the model

frequencyTable

the table of variables ranked by their model frequency

faverageSize

the average size of the models

formulaNetwork

The matrix of interaction between formulas

Jaccard.SM

The Jaccard Stability Measure of the formulas

coefEvolution

The evolution of the coefficients

avgZvalues

The average Z value of each coefficient

featureLocation

The average location of the feature in the formulas

Author(s)

Jose G. Tamez-Pena

See Also

ensemblePredict