This function will take the frequency-ranked of variables and the list of models to create a single bagged model
baggedModel(modelFormulas,
data,
type=c("LM","LOGIT","COX"),
Outcome=NULL,
timeOutcome=NULL,
frequencyThreshold=0.025,
univariate=NULL,
useFreq=TRUE,
n_bootstrap=1,
equifreqCorrection=0
)
the bagged model
the formula of the model
the table of variables ranked by their model frequency
the average size of the models
The matrix of interaction between formulas
The Jaccard Stability Measure of the formulas
The evolution of the coefficients
The average Z value of each coefficient
The average location of the feature in the formulas
The name of the column in data that stores the variable to be predicted by the model
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
Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
The name of the column in data that stores the time to outcome
The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)
set the frequency the threshold of the frequency of features to be included in the model)
The FFRESA.CAD univariate analysis matrix
Use the feature frequency to order the formula terms. If set to a positive value is the number of estimation loops
if greater than 1, defines the number of bootstraps samples to be used
Indicates the average size of repeated features in an equivalent model
Jose G. Tamez-Pena
ensemblePredict