trinROC-package: trinROC: Statistical Tests for Assessing Trinormal ROC Data
In trinROC: Statistical Tests for Assessing Trinormal ROC Data
trinROC-package R Documentation
trinROC: Statistical Tests for Assessing Trinormal ROC Data
Description
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
Details
See vignette("Overview", package = "trinROC") for an overview of the package.
Further, sd(), var() and cov() are chosen with options(trinROC.MLE = TRUE) according to the maximum likelihood estimates (default). Change to sample
estimates by setting options(trinROC.MLE = FALSE)
Author(s)
Maintainer: Annina Cincera annina.cincera@math.uzh.ch
Authors:
Samuel Noll uncle.sam@gmx.net
Reinhard Furrer reinhard.furrer@math.uzh.ch
Other contributors:
Benjamin Reiser [contributor]
Christos T. Nakas cnakas@uth.gr [contributor]
References
Noll, S., Furrer, R., Reiser, B. and Nakas, C. T. (2019).
Inference in ROC surface analysis via a trinormal model-based testing approach.
Stat, 8(1), e249.
See Also
Useful links:
trinROC documentation built on Oct. 29, 2022, 1:12 a.m.
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| trinROC-package | R Documentation |
trinROC: Statistical Tests for Assessing Trinormal ROC Data
Description
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
Details
See vignette("Overview", package = "trinROC") for an overview of the package.
Further, sd(), var() and cov() are chosen with options(trinROC.MLE = TRUE) according to the maximum likelihood estimates (default). Change to sample
estimates by setting options(trinROC.MLE = FALSE)
Author(s)
Maintainer: Annina Cincera annina.cincera@math.uzh.ch
Authors:
Samuel Noll uncle.sam@gmx.net
Reinhard Furrer reinhard.furrer@math.uzh.ch
Other contributors:
Benjamin Reiser [contributor]
Christos T. Nakas cnakas@uth.gr [contributor]
References
Noll, S., Furrer, R., Reiser, B. and Nakas, C. T. (2019). Inference in ROC surface analysis via a trinormal model-based testing approach. Stat, 8(1), e249.
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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