R: Known variance structures

known_vs {asreml}

R Documentation

Known variance structures

Description

Model function associating a known variance structure with a factor in the data.

Usage

vm(obj, source, singG = NULL)

ide(obj)

ric(obj, source, singG = NULL)

Arguments

obj

A factor in data.

source

The known inverse or relationship matrix with the following descriptions:

1. A sparse inverse variance matrix held in three-column coordinate form in row-major order. This triplet matrix must have class ginv from a call to ainverse(), or have attribute INVERSE set to TRUE. For backwards compatibility, a three-column data frame is also accepted. In either case, the source must have a rowNames attribute, or
2. A sparse relationship matrix held in three-column coordinate form (as a matrix) in row-major order. If the attribute INVERSE is not set then FALSE is assumed; a rowNames attribute must be set, or
3. A matrix (or Matrix object) with a dimnames attribute giving the levels of the model term being defined. This may be a relationship matrix or its inverse; if an inverse, it must have an INVERSE attribute set to TRUE, or
4. A numeric vector of the lower triangular elements in row-major order. The vector must have a rowNames attribute, and if an inverse, it must have an INVERSE attribute set to TRUE.

singG

Ignored if source has class ginv or attribute INVERSE = TRUE; in such cases source must be one of:

1. A sparse matrix in coordinate form with class ginv, or attribute INVERSE = TRUE, or
2. An object of class matrix or Matrix with INVERSE = TRUE, or
3. A vector assumed to be the lower triangle in row-major order with attribute INVERSE = TRUE.

If source does not have class ginv, or the attribute INVERSE is FALSE or not set, and singG = NULL, then source is assumed a positive definite relationship matrix and singG is reset to "PD". Otherwise, a character string giving the state of the (to be inverted) source object:

"PD": Positive definite (default).
"ND": source is non-singular indefinite (positive and negative roots). In this case asreml ignores the indefinite condition and proceeds.
"PSD": source is positive semi-definite. In this case, asreml proceeds using Lagrange multipliers to process the matrix. Two cases arise: whether the singularity arises because an effect has zero variance or whether it arises as a linear dependence. An example of the first is when the genomic relationship matrix represents a dominance matrix, and the list of genotypes includes fully inbred individuals (which by definition have no dominance). An example of the second is when the list of genotypes includes clones.
"NSD": source is singular indefinite (positive, zero and negative roots). The indefinite condition is ignored and asreml proceeds using Lagrange multipliers as for "PSD" matrices.

Details

If source is of class Matrix, asreml will convert source internally to either a sparse triplet form (class dsparseMatrix), or dense vector form (class ddenseMatrix) for processing.

Functions

asr_vm(): Create a model term associating a known relationship structure in source with a factor in data.
asr_ide(): Create a term with the levels of vm, and modelled by the homogeneous form of the identity variance structure. The vm term must precede ide in the model for the factor levels to be found.
asr_ric(): Create a model term associating a known relationship structure in source and residual additive genetic effects with a factor in data.

[Package asreml version 4.2.0.480 Index]