pvclass-package: P-Values for Classification

In pvclass: P-Values for Classification

Description

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.

Details

Use cvpvs to compute cross-validated p-values, pvs to classify new observations and analyze.pvs to analyze the p-values.

Author(s)

Niki Zumbrunnen [e-mail]
Lutz Dümbgen [e-mail]
www.imsv.unibe.ch/duembgen/index_ger.html

References

Zumbrunnen N. and Dümbgen L. (2017) pvclass: An R Package for p Values for Classification. Journal of Statistical Software 78(4), 1–19. doi:10.18637/jss.v078.i04

Dümbgen L., Igl B.-W. and Munk A. (2008) P-Values for Classification. Electronic Journal of Statistics 2, 468–493, available at http://dx.doi.org/10.1214/08-EJS245.

Zumbrunnen N. (2014) P-Values for Classification – Computational Aspects and Asymptotics. Ph.D. thesis, University of Bern, available at http://boris.unibe.ch/id/eprint/53585.

Examples

X <- iris[c(1:49, 51:99, 101:149), 1:4]
Y <- iris[c(1:49, 51:99, 101:149), 5]
NewX <- iris[c(50, 100, 150), 1:4]

cv <- cvpvs(X,Y)
analyze.pvs(cv,Y)

pv <- pvs(NewX, X, Y, method = 'k', k = 10)
analyze.pvs(pv)

Example output

            
b               P(b,{})  P(b,{1})   P(b,{2})   P(b,{3}) P(b,{1,2}) P(b,{1,3})
  setosa     0.04081633 0.9591837 0.00000000 0.00000000          0          0
  versicolor 0.00000000 0.0000000 0.95918367 0.04081633          0          0
  virginica  0.00000000 0.0000000 0.04081633 0.91836735          0          0
            
b            P(b,{2,3}) P(b,{1,2,3})
  setosa     0.00000000            0
  versicolor 0.00000000            0
  virginica  0.04081633            0

pvclass documentation built on May 1, 2019, 10:17 p.m.