TBF_ingredients: Ingredients to calculate the TBF

In TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes

Description

This function calculates the ingredients needed to compute the TBFs: like the deviances with their degrees of freedom of the relevant candidate models.

Usage

TBF_ingredients(fullModel = NULL, data, discreteSurv = FALSE,
  numberCores = 1, candidateModels = NULL, package = "nnet",
  maxit = 150)

Arguments

fullModel	formula of the model including all potential variables
data	the data frame with all the information
discreteSurv	Boolean variable telling us whether a 'simple' multinomial regression is looked for or if the goal is a discrete survival-time model for multiple modes of failure is needed.
numberCores	How many cores should be used in parallel?
candidateModels	Instead of defining the full model we can also specify the candidate models whose deviance statistic and d.o.f should be computed
package	Which package should be used to fit the models; by default the nnet package is used; we could also specify to use the package 'VGAM'
maxit	Only needs to be specified with package nnet: maximal number of iterations

Value

an object of class TBF.ingredients

Author(s)

Rachel Heyard

TBFmultinomial documentation built on May 2, 2019, 2:11 p.m.