PMP: Posterior model probability

In TBFmultinomial: TBF Methodology Extension for Multinomial Outcomes

Description

This function computes the posterior probability of all candidate models

Usage

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?

Value

an object of class TBF.ingredients

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)

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