emma.MLE {emma}R Documentation

Maximum likelihood estimation using linear mixed model

Description

Estimates maximum likelihood and its parameters using linear mixed model

Usage

  emma.MLE (y, X, K, Z=NULL, ngrids=100, llim=-10, ulim=10,
  esp=1e-10, eig.L = NULL, eig.R = NULL)

Arguments

y a size n vector containing response variables (or phenotypes), where n is the number of individuals
X a n by p size matrix containing fixef effects (including mean,snps), where n is the number of individuals, and p is the number of fixed effects
K A t by t matrix of kinship coefficients, representing the pairwise genetic relatedness between strains
Z A n by t incidence matrix mapping each individual to a strain. If this is NULL, n and t should be equal and an identity matrix replace Z
ngrids Number of grids to search optimal variance component
llim Lower bound of log ratio of two variance components
ulim Upper bound of log ratio of two variance components
esp Tolerance of numerical precision error
eig.L Eigenvector from K used in ML estimate. If specified, it may avoid redundant computation inside the function
eig.R Eigenvector from x and K used in ML estimate. If specified, it may avoid redundant computation inside the function

Details

The following criteria must hold; otherwise an error occurs - [length of y] == [# rows in Z] == [# rows in X] - [# cols in Z] == [# rows in K] == [# cols in K] - rowSums(Z) should be a vector of ones - colSums(Z) should not contain zero elements - K must be a positive semidefinite matrix

Value

A list containing:

ML Maximum likleihood estimate of the data given the linear mixed model
delta Ratio between genetic and random variance component
vg The coefficients of genetic variance component
ve The coefficients of random variance component

Author(s)

Hyun Min Kang h3kang@cs.ucsd.edu

References

Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, and Eskin E, Efficient Control of Population Structure in Model Organism Association Mapping, Genetics 178:1709-1723, 2008

See Also

emma.ML.LRT,emma.kinship,emma.REMLE,emma.MLE.noX

Examples

  ## Not run: 
    ## Load data
    data(emmadat)

    ## Run EMMA MLE
    rs <- emma.MLE(emmadat$ys[1,],cbind(1,emmadat$xs[1,]),emmadat$K)

    ## return maximum likelihood
    rs$ML
  ## End(Not run)

[Package emma version 1.1.2 Index]