emma.MLE {emma} | R Documentation |
Estimates maximum likelihood and its parameters using linear mixed model
emma.MLE (y, X, K, Z=NULL, ngrids=100, llim=-10, ulim=10, esp=1e-10, eig.L = NULL, eig.R = NULL)
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 |
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
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 |
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.ML.LRT
,emma.kinship
,emma.REMLE
,emma.MLE.noX
## 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)