Description Details Author(s) References Examples
Implements multiresponse permutation procedure (MRPP), variable importance measure for MRPP, and backward variable selection procedures for the MRPP, applicable to high dimensional data.
Package: | MRPP |
Type: | Package |
Version: | 1.0-321 |
Date: | 2013-11-17 |
License: | GPL version 2 or newer |
LazyLoad: | yes |
mrpp.test
The MRPP, a permutation test applicable to high dimensional data.
get.dp.dw.kde
Variable importance measure: approximate derivative of p-values to weights.
get.p.dd.dw
Variable importance measure: permutation p-value using the derivative of Euclidean distance with respect to weights as the distance measure.
mrppBVS
Backward variable selection for the MRPP.
mrppBVS.test
Backward variable selection nested within outer permutations.
smrpp.test
Sparse Weighted MRPP within outer permutations.
smrpp.penWt
Sparse Weights given MRPP importance measures from get.dp.dw.kde
.
permuteTrt
Generate random treatment assignments.
FR2permvec
Conversions among decimal factoradic number, radix factoradic number, and permutation vector.
HSEL.bigz
Generate a random sample of size m from a large population of size n.
bw.mse.pdf.asym
Choose a bandwidth for kernel density estimation by minimizing asymptotic mean squared error of density estimate at the first data point.
Long Qu
Maintainer: Long Qu <long.qu@wright.edu>
Long Qu, Dan Nettleton, and Jack C. M. Dekkers. Relative Variable Importance and Backward Variable Selection for the Multiresponse Permutation Procedure, with Applications to High Dimensional Genomic Data. (Manuscript under review)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | set.seed(2340)
x=matrix(rnorm(20*5),20)
trt=gl(2,10)
nparts( table(trt)) ## 92378 partitions = choose(20,10)/2
urand.bigz(0,seed=1032940L) # init seed
pmat=permuteTrt(trt, 5e2L) ## use 500 random permutations
mrpp.test(dist(x), trt, permutedTrt=pmat, wtmethod=0 ) ## MRPP for all variables
## Not run:
mrppBVS(x, pmat) ## backward selection of variables
mrppBVS.test(x, pmat) ## nested permutation test with backward variable selection
smrpp.test(x, trt, permutedTrt=pmat, wtmethod=0 )
## End(Not run)
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