This function will take the frequency-ranked set of variables and will generate a new model with terms that meet either the integrated discrimination improvement (IDI), or the net reclassification improvement (NRI), threshold criteria.
updateModel.Bin(Outcome,
covariates = "1",
pvalue = c(0.025, 0.05),
VarFrequencyTable,
variableList,
data,
type = c("LM", "LOGIT", "COX"),
lastTopVariable = 0,
timeOutcome = "Time",
selectionType = c("zIDI","zNRI"),
maxTrainModelSize = 0,
zthrs = NULL
)The name of the column in data that stores the variable to be predicted by the model
A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates)
The maximum p-value, associated to either IDI or NRI, allowed for a term in the model
An array with the ranked frequencies of the features, (e.g. the ranked.var value returned by the ForwardSelection.Model.Bin function)
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
A data frame where all variables are stored in different columns
Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX")
The maximum number of variables to be tested
The name of the column in data that stores the time to event (needed only for a Cox proportional hazards regression model fitting)
The type of index to be evaluated by the improveProb function (Hmisc package): z-score of IDI or of NRI
Maximum number of terms that can be included in the model
The z-thresholds estimated in forward selection
An object of class lm, glm, or coxph containing the final model
A vector with the names of the features that were included in the final model
An object of class formula with the formula used to fit the final model
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