emma.ML.LRT {emma} | R Documentation |
Performs an efficient linear mixed model association mapping via Likelihood Ratio test (LR) after estimating variance component using REML.
emma.REML.LRT (ys, xs, K, Z=diag(ncol(xs)), X0 = matrix(1,nrow(xs),1), ngrid=100, llim=-5, ulim=5, esp=1e-10, ponly = FALSE)
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.L |
Eigenvector from Z and K used in ML estimate. If specified, it may avoid redundant computation inside the function |
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 |
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
A list containing:
ps |
The g by m matrix of p-values between every pair of indicator-response variables |
stats |
The g by m matrix of chi-square statistic values |
ML0s |
The g by m matrix of log maximum likelihood under null hypothesis |
ML1s |
The g by m matrix of log maximum likliehood under alternative hypothesis |
vgs |
The g by m matrix of genetic variance components under null hypothesis |
ves |
The g by m matrix of random variance components under null hypothesis |
if ponly is TRUE, only ps is return as matrix form.
Hyun Min Kang h3kang@cs.ucsd.edu
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
emma.REML.t
,emma.kinship
,emma.MLE
## Not run: ## Load data data(emmadat) ## Run EMMA rs <- emma.ML.LRT(emmadat$ys,emmadat$xs,emmadat$K) ## return p-values rs$ps ## End(Not run)