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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