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.
|Depends:||R (≥ 2.15.1), doParallel|
|Imports:||ROCR, irlba, parallel, foreach|
|Author:||Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy|
|Maintainer:||Hae Kyung Im <haky at uchicago.edu>|
|License:||GPL (≥ 3)|
|CRAN checks:||OmicKriging results|
Application Tutorial: OmicKriging
|Windows binaries:||r-devel: OmicKriging_1.4.0.zip, r-release: OmicKriging_1.4.0.zip, r-oldrel: OmicKriging_1.4.0.zip|
|OS X El Capitan binaries:||r-release: OmicKriging_1.4.0.tgz|
|OS X Mavericks binaries:||r-oldrel: OmicKriging_1.4.0.tgz|
|Old sources:||OmicKriging archive|
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