It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.

Version: | 1.4.0 |

Depends: | R (≥ 2.15.1), doParallel |

Imports: | ROCR, irlba, parallel, foreach |

Published: | 2016-03-08 |

Author: | Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy |

Maintainer: | Hae Kyung Im <haky at uchicago.edu> |

License: | GPL (≥ 3) |

NeedsCompilation: | no |

Materials: | README |

CRAN checks: | OmicKriging results |

Reference manual: | OmicKriging.pdf |

Vignettes: |
Application Tutorial: OmicKriging |

Package source: | OmicKriging_1.4.0.tar.gz |

Windows binaries: | r-devel: OmicKriging_1.4.0.zip, r-release: OmicKriging_1.4.0.zip, r-oldrel: OmicKriging_1.4.0.zip |

macOS binaries: | r-release: OmicKriging_1.4.0.tgz, r-oldrel: OmicKriging_1.4.0.tgz |

Old sources: | OmicKriging archive |

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