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,
  Dim = 2,
  TS,
  TSextra,
  With.p.value = FALSE,
  nsample = 250,
  nbins = c(5, 5),
  alpha = 0.05,
  param_alt,
  SuppressMessages = TRUE,
  B = 1000,
  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?
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	(list of) values of parameter under the alternative hypothesis. If missing included values are used.
SuppressMessages	=TRUE, should informative messages be shown?
B	= 1000, number of simulation runs.
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(Continuous=TRUE, WithEstimation=FALSE, 
           study=1:3, 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(Continuous=FALSE, WithEstimation=FALSE, 
           study=1:3, param_alt=p,alpha=0.1, B=25, maxProcessor = 1)         

MDgof documentation built on Feb. 13, 2026, 1:06 a.m.