EM_REML_MM

numeric matrix; the inverse of the kernel matrix

Mat_K_inv

numeric matrix; design matrix of predictors with fixed effects

numeric matrix; design matrix of predictors with random effects

numeric scalars;
		initial guess values, associated to the mixed model variance parameters,
		for the EM-REML algorithm

init_sigma2K, init_sigma2E

convergence precision (i.e. tolerance) associated to the mixed model 
		variance parameters, for the EM-REML algorithm, and number of maximum 
		iterations allowed if convergence is not reached

convergence_precision, nb_iter

boolean (TRUE or FALSE character string); 
		should estimated components be displayed at each iteration

display

EM_REML_MM estimates the components and variance parameters of the following mixed model; Y =X*Beta + Z*U + E, using the EM-REML algorithm.

Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.

Laval Jacquin

KRMM

Kernel Ridge Mixed Model

EM_REML_MM function

Expectation-Maximization (EM) algorithm for the restricted maximum likelihood (REML) associated to the mixed model
 — EM_REML_MM

Expectation-Maximization (EM) algorithm for the restricted maximum likelihood (REML) associated to the mixed model

EM_REML_MM: Expectation-Maximization (EM) algorithm for the restricted maximum likelihood (REML) associated to the mixed model

Description

Usage

Arguments

Value

References