cvmgof: an R package for Cramér-von Mises goodness-of-fit tests in regression models

Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly heteroscedastic) regression model. Most of them are 'directional' in that they detect departures from a given assumption of the model. Other tests are 'global' (or 'omnibus'...

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Bibliographic Details
Published in:Journal of statistical computation and simulation 2022-04, Vol.92 (6), p.1246-1266
Main Authors: Azaïs, R., Ferrigno, S., Martinez, M.-J.
Format: Article
Language:English
Subjects:
bandwidth
Cramér-von Mises statistic
Goodness of fit
Goodness-of-fit test
Model testing
nonparametric regression
Nonparametric statistics
regression function
Regression models
Statistical analysis
Statistical tests
Statistics
wild bootstrap
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Summary:Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly heteroscedastic) regression model. Most of them are 'directional' in that they detect departures from a given assumption of the model. Other tests are 'global' (or 'omnibus') in that they assess whether a model fits a dataset on all its assumptions. We focus on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. We consider two nonparametric 'directional' tests and one nonparametric 'global' test, all based on generalizations of the Cramér-von Mises statistic. To perform these goodness-of-fit tests, we develop the R package cvmgof providing an easy-to-use tool for practitioners, available from the Comprehensive R Archive Network (CRAN). The use of the library is illustrated through a tutorial on real data. A simulation study is carried out in order to show how the package can be exploited to compare the three implemented tests.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2021.1991346