prototest: Inference on Prototypes from Clusters of Features

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Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.

Author
Stephen Reid
Date of publication
2016-04-19 08:59:44
Maintainer
Stephen Reid <sreid@stanford.edu>
License
GPL (>= 2)
Version
1.1
URLs

View on CRAN

Man pages

print.prototest
Print 'prototest' object
prototest.multivariate
Perform Prototype or F tests for Significance of Groups of...
prototest-package
Inference on Prototypes from Clusters of Features
prototest.univariate
Perform Prototype or F Tests for Significance of Groups of...

Files in this package

prototest
prototest/src
prototest/src/Makevars
prototest/src/hit_and_run.cpp
prototest/src/multivariate_alr.cpp
prototest/src/RcppExports.cpp
prototest/src/multivariate_elr.cpp
prototest/src/univariate_lr.cpp
prototest/NAMESPACE
prototest/R
prototest/R/nonselective.multivariate.F.test.R
prototest/R/compute.test.statistic.R
prototest/R/compute.lr.stat.multi.R
prototest/R/find.root.R
prototest/R/maximise.lr.R
prototest/R/compute.selective.ts.and.p.val.R
prototest/R/compute.QRS.vectors.R
prototest/R/interval.if.derivative.always.positive.R
prototest/R/update.mu.R
prototest/R/compute.trunc.F.test.p.value.R
prototest/R/enet.selection.A.b.R
prototest/R/limits.exact.lr.R
prototest/R/compute.mc.t.statistic.R
prototest/R/update.theta.R
prototest/R/update.sigma.R
prototest/R/fit.enet.fixed.lambda.R
prototest/R/compute.lr.stat.R
prototest/R/update.theta.approx.R
prototest/R/compute.approx.lr.R
prototest/R/compute.non.selective.p.val.R
prototest/R/limits.approx.lr.R
prototest/R/print.prototest.R
prototest/R/find.overall.truncation.interval.R
prototest/R/compute.F.statistic.R
prototest/R/hit.and.run.samples.multivariate.model.R
prototest/R/find.where.sign.changes.R
prototest/R/RcppExports.R
prototest/R/truncation.region.function.R
prototest/R/gradient.truncation.region.function.R
prototest/R/mc.selection.A.b.R
prototest/R/enet.selection.A.b.from.enet.R
prototest/R/find.norm.limits.R
prototest/R/find.single.truncation.interval.R
prototest/R/interval.if.derivative.always.negative.R
prototest/R/find.limits.R
prototest/R/prototest.multivariate.R
prototest/R/compute.mc.t.p.val.R
prototest/R/nonselective.mc.test.R
prototest/R/prototest.univariate.R
prototest/R/compute.exact.lr.R
prototest/MD5
prototest/build
prototest/build/partial.rdb
prototest/DESCRIPTION
prototest/man
prototest/man/prototest.multivariate.Rd
prototest/man/prototest.univariate.Rd
prototest/man/print.prototest.Rd
prototest/man/prototest-package.Rd