PIPs_by_landmarking function

Posterior inclusion probabilities (PIPs) by landmarking

This function gives us the PIPs for each landmark.

PIPs_by_landmarking(fullModel, data, discreteSurv = TRUE, numberCores = 1,
  package = "nnet", maxit = 150, prior = "flat", method = "LEB",
  landmarkLength = 1, lastlandmark, timeVariableName)

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?
• 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
• prior: Prior on the model space
• method: Method for the g definition
• landmarkLength: Length of the landmark, by default we use each day
• lastlandmark: Where will be the last landmark?
• timeVariableName: What is the name of the variable indicating time?

Returns

a list with the PIPs for each landmark

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

PIPs_landmark <- PIPs_by_landmarking(fullModel = FULL, data = VAP_data,
                                     discreteSurv = TRUE, numberCores = 1,
                                     package = 'nnet', maxit = 150,
                                     prior = 'flat',  method = 'LEB',
                                     landmarkLength = 7, lastlandmark = 21,
                                     timeVariableName = 'day')

Author(s)

Rachel Heyard