postInclusionProb: Posterior inclusion probability (PIP)

In TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes

Description

This function computes the PIPs of all potential predictors

Usage

postInclusionProb(object)

Arguments

object

An object of class PMP

Value

an named vector with all PIPs

Author(s)

Rachel Heyard

Examples

# extract the data:
data("VAP_data")

# 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

# computation of the posterior model probabilities:
test <- PMP(fullModel = FULL, data = VAP_data,
            discreteSurv = TRUE, maxit = 150)
class(test)

#computation of the posterior inclusion probabilities:
postInclusionProb(test)

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