Computes nonparametric p-values for the potential class memberships of new observations as well as cross-validated p-values for the training data. The p-values are based on permutation tests applied to an estimated Bayesian likelihood ratio, using a plug-in statistic for the Gaussian model, 'k nearest neighbors', 'weighted nearest neighbors' or 'penalized logistic regression'. Additionally, it provides graphical displays and quantitative analyses of the p-values.
|Author||Niki Zumbrunnen <email@example.com>, Lutz Duembgen <firstname.lastname@example.org>.|
|Date of publication||2015-11-03 10:59:08|
|Maintainer||Niki Zumbrunnen <email@example.com>|
|License||GPL (>= 2)|
analyze.pvs: Analyze P-Values
buerk: Medical Dataset
cvpvs: Cross-Validated P-Values
cvpvs.gaussian: Cross-Validated P-Values (Gaussian)
cvpvs.knn: Cross-Validated P-Values (k Nearest Neighbors)
cvpvs.logreg: Cross-Validated P-Values (Penalized Multicategory Logistic...
cvpvs.wnn: Cross-Validated P-Values (Weighted Nearest Neighbors)
pvclass-package: P-Values for Classification
pvs: P-Values to Classify New Observations
pvs.gaussian: P-Values to Classify New Observations (Gaussian)
pvs.knn: P-Values to Classify New Observations (k Nearest Neighbors)
pvs.logreg: P-Values to Classify New Observations (Penalized...
pvs.wnn: P-Values to Classify New Observations (Weighted Nearest...
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