Description Usage Arguments Value Author(s) References
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.
1 2 3 4 5 6 | EM_REML_MM( Mat_K_inv, Y, X, Z, init_sigma2K,
init_sigma2E, convergence_precision,
nb_iter, display )
|
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 |
Beta_hat |
Estimated fixed effect(s) |
Sigma2K_hat, Sigma2E_hat |
Estimated variance components |
Laval Jacquin <jacquin.julien@gmail.com>
Foulley, J.-L. (2002). Algorithme em: théorie et application au modèle mixte. Journal de la Société française de Statistique 143, 57-109
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