model_priors: Prior model probability

In TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes

Description

This function computes the prior model probabilities of the candidate models

Usage

model_priors(fullModel, discreteSurv = TRUE, modelPrior = "flat")

Arguments

fullModel	formula of the model including all potential variables
discreteSurv	Boolean var 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.
modelPrior	what prior should be used on the model space? modelPrior should be included in {'flat','dependent'} where 'flat' means a uniform pior and 'dependent' sets a multiplicity-corrected model prior on the model space.

Value

a numerical vector with the prior model probabilities

Author(s)

Rachel Heyard

Examples

# the definition of the full model with three potential predictors:
FULL <- outcome ~ ns(day, df = 4) + gender + type + SOFA
# here we define time as a spline with 3 knots

priors <- model_priors(fullModel = FULL, discreteSurv = TRUE,
                       modelPrior = 'dependent')

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