R/loglikVAR1.r

In wvanwie/ragt2ridges: Ridge Estimation of Vector Auto-Regressive (VAR) Processes

loglikVAR1 <- function(Y, 
                       A, 
                       P, 
                       unbalanced=matrix(nrow=0, ncol=2)){

	########################################################################
	# 
	# DESCRIPTION:
	# Log-likelihood of the VAR(1) model specified by the supplied 
	# parameters.
	#
	# ARGUMENTS: 
	# -> Y            : Three-dimensional array containing the data. The 
	#                   first, second and third dimensions correspond to 
	#                   covariates, time and samples, respectively. The 
	#                   data are assumed to centered covariate-wise. 
	# -> A            : Matrix A of regression parameters.
	# -> P            : Inverse error covariance matrix.
	# -> unbalanced   : A matrix with two columns, indicating the 
	#                   unbalances in the design. Each row represents a 
	#                   missing design point in the 
	#                   (time x individual)-layout. 
	#                   The first and second column indicate the time and 
	#                   individual (respectively) specifics of the missing 
	#                   design point.
	#
	# DEPENDENCIES:
	# ...
	#
	# NOTES:
	# ...
	#
	########################################################################

	# input checks
	if (as.character(class(Y)) != "array"){ 
		stop("Input (Y) is of wrong class.") 
	}
	if (length(dim(Y)) != 3){ 
		stop("Input (Y) is of wrong dimensions: either covariate, time or sample dimension is missing.") 
	}
	if (as.character(class(A)) != "matrix"){ 
		stop("Input (A) is of wrong class.") 
	}
	if (as.character(class(P)) != "matrix"){ 
		stop("Input (P) is of wrong class.") 
	}
	if (!all(eigen(P)$values > 0)){ 
		stop("Non positive-definite precision matrix is provided.") 
	}
	if (nrow(A) != ncol(A)){ 
		stop("Matrix A is not square.") 
	}
	if (nrow(A) != nrow(P)){ 
		stop("Dimensions precision matrix and A do not match.") 
	}
	if (as.character(class(unbalanced)) != "matrix"){ 
		stop("Input (unbalanced) is of wrong class.") 
	}    
	if (ncol(unbalanced) != 2){ 
		stop("Wrong dimensions of the matrix unbalanced.") 
	} 

	# set profiles of missing (time, sample)-points to missing
	Y <- .armaVAR_array2cube_withMissing(Y, 
                                             unbalanced[,1], 
	                                     unbalanced[,2])

	# obtain loglikelihood contribution of residuals 
	LL <- .armaVAR1_loglik(Y, A, P)
	return(LL)
}