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.
|Author||Hae Kyung Im, Heather E. Wheeler, Keston Aquino Michaels, Vassily Trubetskoy|
|Date of publication||2016-03-08 00:12:43|
|Maintainer||Hae Kyung Im <firstname.lastname@example.org>|
|License||GPL (>= 3)|
krigr_cross_validation: Multithreaded cross validation routine for Omic Kriging.
load_sample_data: Loads sample phenotype and covariate data into data frame.
make_GXM: Compute gene expression correlation matrix.
make_PCs_irlba: Run Principal Component Analysis (PCA) using the irlba...
make_PCs_svd: Run Principal Component Analysis (PCA) using base R svd()...
okriging: Run omic kriging on a set of correlation matrices and a given...
read_GRMBin: Read the GRM binary file.
write_GRMBin: Write GRM binary files.