Graphical displays and quantitative analyses of a matrix of p-values.
analyze.pvs(pv, Y = NULL, alpha = 0.05, roc = TRUE, pvplot = TRUE, cex = 1)Table containing empirical conditional inclusion and/or pattern probabilities for each class \(b\). In case of \(L = 2\) or \(L=3\) classes, all patterns \(S\) are considered. In case of \(L > 3\), all inclusion probabilities and some special patters \(S\) are considered.
matrix with p-values, e.g. output of cvpvs or pvs.
optional. Vector indicating the classes which the observations belong to.
test level, i.e. 1 - confidence level.
logical. If TRUE and Y is not NULL, ROC curves are plotted.
logical. If TRUE or Y is NULL, the p-values are displayed graphically.
A numerical value giving the amount by which plotting text should be magnified relative to the default.
Niki Zumbrunnen niki.zumbrunnen@gmail.com
Lutz Dümbgen lutz.duembgen@stat.unibe.ch
https://www.imsv.unibe.ch/about_us/staff/prof_dr_duembgen_lutz/index_eng.html
Displays the p-values graphically, i.e. it plots for each p-value a rectangle. The area of this rectangle is proportional to the the p-value. The rectangle is drawn blue if the p-value is greater than alpha and red otherwise.
If Y is not NULL, i.e. the class memberships of the observations are known (e.g. cross-validated p-values), then additionally it plots the empirical ROC curves and prints some empirical conditional inclusion probabilities \(I(b,\theta)\) and/or pattern probabilities \(P(b,S)\). Precisely, \(I(b,\theta)\) is the proportion of training observations of class \(b\) whose p-value for class \(\theta\) is greater than \(\alpha\), while \(P(b,S)\) is the proportion of training observations of class \(b\) such that the \((1 - \alpha)\)-prediction region equals \(S\).
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 tools:::Rd_expr_doi("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.
cvpvs, pvs
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)
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