OmicKriging: Poly-Omic Prediction of Complex TRaits
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 |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=OmicKriging
to link to this page.