bioconductor-multtest

Summary

Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

Versions

  • 2.26.0

License

LGPL

Meta

package:
  name: bioconductor-multtest
  version: 2.26.0
source:
  fn: multtest_2.26.0.tar.gz
  url: http://bioconductor.org/packages/release/bioc/src/contrib/multtest_2.26.0.tar.gz
  md5: 77592f4a4f61628d00bb356da06b6179
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
requirements:
  build:
    - bioconductor-biobase
    - bioconductor-biocgenerics
    - 'r >=2.10'
  run:
    - bioconductor-biobase
    - bioconductor-biocgenerics
    - 'r >=2.10'
test:
  commands:
    - '$R -e "library(''multtest'')"'
about:
  home: http://bioconductor.org/packages/release/bioc/html/multtest.html
  license: LGPL
  summary: 'Non-parametric bootstrap and permutation resampling-based multiple testing
    procedures (including empirical Bayes methods) for controlling the family-wise
    error rate (FWER), generalized family-wise error rate (gFWER), tail probability
    of the proportion of false positives (TPPFP), and false discovery rate (FDR).  Several
    choices of bootstrap-based null distribution are implemented (centered, centered
    and scaled, quantile-transformed). Single-step and step-wise methods are available.
    Tests based on a variety of t- and F-statistics (including t-statistics based
    on regression parameters from linear and survival models as well as those based
    on correlation parameters) are included.  When probing hypotheses with t-statistics,
    users may also select a potentially faster null distribution which is multivariate
    normal with mean zero and variance covariance matrix derived from the vector influence
    function.  Results are reported in terms of adjusted p-values, confidence regions
    and test statistic cutoffs. The procedures are directly applicable to identifying
    differentially expressed genes in DNA microarray experiments.'