emma.REML.t {emma}R Documentation

Linear mixed model association via t-test with REML estimates.

Description

Performs an efficient linear mixed model association mapping via t-test after estimating variance component using REML.

Usage

  emma.REML.t (ys, xs, K, Z=diag(ncol(ys)),
  X0 = matrix(1,nrow(ys),1), ngrids=100, llim=-5, ulim=5,
  esp=1e-10, ponly = FALSE, eigen.R0 = NULL, eigen.R1 = NULL)

Arguments

ys A g by n matrix, where g is the number of response variables (or phenotypes), and n is the number of individuals
xs A m by t matrix, where m is number of indicator variables (or snps), and n is the number of strains
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
X0 A n by p matrix of fixed effects variables, where p is the number of fixed effects including mean and other confounding variables
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
ponly Returns p-value matrix only if TRUE
eig.R0 Eigenvector from X0, Z and K used in REML estimate. If specified, it may avoid redundant computation inside the function
eig.R1 Eigenvector from X1, Z and K used in REML estimate. If specified, it may avoid redundant computation inside the function. Valid only when m=1

Details

The following criteria must hold; otherwise an error occurs - [# cols in ys] == [# rows in Z] == [# rows in X0] - [# cols in xs] == [# 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:

ps The m by g matrix of p-values between every pair of indicator-response variables
REMLs The g by m matrix of restricted maximum likelihoods
stats The g by m matrix of t-statistic values
dfs The m by g matrix of degrees of freedoms
vgs The m by g matrix of genetic variance components in REML estimates
ves The m by g matrix of random variance components in REML estimates


if ponly is TRUE, only ps is return as matrix form.

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

Examples

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

    ## Run EMMA
    rs <- emma.REML.t(emmadat$ys,emmadat$xs,emmadat$K)

    ## return p-values
    rs
  ## End(Not run)

[Package emma version 1.1.2 Index]