okriging {OmicKriging}

Run omic kriging on a set of correlation matrices and a given phenotype.

Description

Universal kriging formula: lambda' = ( c + X m )' iSig m' = ( x - X' iSig c )' ( X' iSig X )^-1 m' = ( t(x) - c' iSig X ) ( X' iSig X )^-1 lambda' = (c' + m' X) iSig x: #covariates x ntest X: ntrain x #cov c: ntrain x ntest

Usage

okriging(idtest, idtrain = NULL, corlist, H2vec, pheno, phenoname,
  Xcova = NULL)

Arguments

idtest

A vector of sample IDs which constitute the test set.

idtrain

A vector of sample IDs which constitute the training set.

corlist

A list of correlation matrices used in Kriging. rownames and colnames of cor should be IID list and include idtest and idtrain.

H2vec

has weights for each RM relatednes matrix

pheno

A data frame with rownames set as sample IDs and a column containing phenotype data.

phenoname

The name of the column in pheno which contains phenotype data to test.

Xcova

Data frame of covariates with rownames() set to sample IDs.

Value

A dataframe with three columns: sample ID, observed phenotype Ytest, and predicted phenotype Ypred

References

Cressie 1993 Statistics for Spatial Data p.154