run.studies: Benchmarking for Multivariate Goodness-of-fit Tests

In MDgof: Various Methods for the Goodness-of-Fit Problem in D>1 Dimensions

Benchmarking for Multivariate Goodness-of-fit Tests

Description

This function runs the case studies included in the package.

Usage

run.studies(
  study,
  Continuous = TRUE,
  WithEstimation = FALSE,
  Hybrid = FALSE,
  nMC5 = FALSE,
  Dim = 2,
  TS,
  TSextra,
  With.p.value = FALSE,
  nsample = 250,
  nbins = c(5, 5),
  alpha = 0.05,
  param_alt,
  SuppressMessages = FALSE,
  ShowResult = TRUE,
  B = 1000,
  seed = NULL,
  maxProcessor
)

Arguments

study	either the name of the study, or its number in the list. If missing all the studies are run.
Continuous	=TRUE, run cases for continuous data.
WithEstimation	=FALSE, run case studies with or without parameter estimation?
Hybrid	=FALSE run hybrid tests?
nMC5	=FALSE, sample size of hybrid test.
Dim	=2 two or five-dimensional continuous data sets?
TS	routine to calculate new test statistics.
TSextra	list passed to TS (optional).
With.p.value	=FALSE, does user supplied routine return p values?
nsample	= 250, desired sample size. 250 is used in included case studies.
nbins	=c(5,5) number of bins for discretized data.
alpha	=0.05, type I error probability of tests. 0.05 is used in included case studies.
param_alt	vector or matrix of values of parameters under the alternative hypothesis. If missing included values are used.
SuppressMessages	=FALSE, should informative messages be shown?
ShowResult	=TRUE should result be shown in console?
B	= 1000, number of simulation runs.
seed	=NULL (optional) seed for simulations, to aid reproducability
maxProcessor	number of cores to use. If missing the number of physical cores-1 is used. If set to 1 no parallel processing is done.

Details

For details consult vignette(package="MDgof")

Value

A (list of ) matrices of p.values.

Examples

#Examples are run with a super small B=25 simulation runs to satisfy CRAN submission rules.
#Run a new test for studies 1-3 for continuous data and without estimation.
#The new test is an (included) chi square test that finds it's own p value.
TSextra=list(Continuous=TRUE, WithEstimation=FALSE, Withpvalue=TRUE)
MDgof::run.studies(study=1:3, Continuous=TRUE, WithEstimation=FALSE, 
           TS=MDgof::newTS, TSextra=TSextra, 
           With.p.value = TRUE, B=25, maxProcessor = 1)
#Run included tests for studies 1-3 for discrete data and without estimation,
#but with type I error alpha=0.1
p=MDgof::power_studies_disc_results[[3]][1:3,,drop=FALSE]    
MDgof::run.studies(study=1:3, Continuous=FALSE, WithEstimation=FALSE, 
           param_alt=p,alpha=0.1, B=25, maxProcessor = 1)         

MDgof documentation built on Sept. 23, 2026, 5:08 p.m.