okriging

A vector of sample IDs which constitute the test set.

idtest

A vector of sample IDs which constitute the training set.

idtrain

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

corlist

has weights for each RM relatednes matrix

H2vec

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

pheno

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

phenoname

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

Xcova


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


prediction

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.

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

Description

Usage

Arguments

Value

References