EM_REML_MM: Expectation-Maximization (EM) algorithm for the restricted...

In KRMM: Kernel Ridge Mixed Model

Description

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.

Usage

		EM_REML_MM( Mat_K_inv, Y, X, Z, init_sigma2K, 
		
		init_sigma2E, convergence_precision, 
		
		nb_iter, display )		
	

Arguments

Mat_K_inv	numeric matrix; the inverse of the kernel matrix
Y	numeric vector; response vector
X	numeric matrix; design matrix of predictors with fixed effects
Z	numeric matrix; design matrix of predictors with random effects
init_sigma2K, init_sigma2E	numeric scalars; initial guess values, associated to the mixed model variance parameters, for the EM-REML algorithm
convergence_precision, nb_iter	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
display	boolean (TRUE or FALSE character string); should estimated components be displayed at each iteration

Value

Beta_hat	Estimated fixed effect(s)
Sigma2K_hat, Sigma2E_hat	Estimated variance components

Author(s)

Laval Jacquin <[e-mail]>

References

Foulley, J.-L. (2002). Algorithme em: théorie et application au modèle mixte. Journal de la Société française de Statistique 143, 57-109

KRMM documentation built on May 2, 2019, 2:50 p.m.