PMP function

Posterior model probability

This function computes the posterior probability of all candidate models

PMP(fullModel = NULL, candidateModels = NULL, data = NULL,
  discreteSurv = TRUE, modelPrior = NULL, method = "LEB",
  prior = "flat", package = "nnet", maxit = 150, numberCores = 1)

Arguments

• fullModel: formula of the model including all potential variables
• candidateModels: Instead of defining the full model we can also specify the candidate models whose deviance statistic and d.o.f should be computed
• 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.
• modelPrior: optionaly the model priors can be computed before if candidateModels is different from NULL.
• method: tells us which method for the definition of g should be used. Possibilities are: LEB, GEB, g=n, hyperG, ZS, ZSadapted and hyperGN
• prior: should a dependent or a flat prior be used on the model space? Only needed if ``method = `GEB```.
• 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
• numberCores: How many cores should be used in parallel?

Returns

an object of class TBF.ingredients

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)

Author(s)

Rachel Heyard