Description Usage Arguments Details Value Author(s) References See Also Examples
Computes nonparametric p-values for the potential class memberships of new observations. The p-values are based on a plug-in statistic for the standard Gaussian model. The latter means that the conditional distribution of X, given Y=y, is Gaussian with mean depending on y and a global covariance matrix.
1 | pvs.gaussian(NewX, X, Y, cova = c('standard', 'M', 'sym'))
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NewX |
data matrix consisting of one or several new observations (row vectors) to be classified. |
X |
matrix containing training observations, where each observation is a row vector. |
Y |
vector indicating the classes which the training observations belong to. |
cova |
estimator for the covariance matrix: |
Computes nonparametric p-values for the potential class memberships of new observations. Precisely, for each new observation NewX[i,]
and each class b
the number PV[i,b]
is a p-value for the null hypothesis that Y[i] = b.
This p-value is based on a permutation test applied to an estimated Bayesian likelihood ratio, using a plug-in statistic for the standard Gaussian model with estimated prior probabilities N(b)/n. Here N(b) is the number of observations of class b and n is the total number of observations.
PV
is a matrix containing the p-values. Precisely, for each new observation NewX[i,]
and each class b
the number PV[i,b]
is a p-value for the null hypothesis that Y[i] = b.
Niki Zumbrunnen niki.zumbrunnen@gmail.com
Lutz Dümbgen lutz.duembgen@stat.unibe.ch
www.imsv.unibe.ch/duembgen/index_ger.html
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.
pvs, pvs.knn, pvs.wnn, pvs.logreg
1 2 3 4 5 | 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]
pvs.gaussian(NewX, X, Y, cova = 'standard')
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