sommer (version 4.3.5)

dsc: diagonal covariance structure

Description

dsc creates a diagonal covariance structure for the levels of the random effect to be used with the mmec solver.

Usage

dsc(x, thetaC=NULL, theta=NULL)

Value

$res

a list with the provided vector and the variance covariance structure expected for the levels of the random effect.

Arguments

x

vector of observations for the random effect.

thetaC

an optional symmetric matrix for constraints in the variance-covariance components. The symmetric matrix should have as many rows and columns as the number of levels in the factor 'x'. The values in the matrix define how the variance-covariance components should be estimated:

0: component will not be estimated

1: component will be estimated and constrained to be positive

2: component will be estimated and unconstrained

3: component will be fixed to the value provided in the theta argument

theta

an optional symmetric matrix for initial values of the variance-covariance components. When providing customized values, these values should be scaled with respect to the original variance. For example, to provide an initial value of 1 to a given variance component, theta would be built as:

theta = matrix( 1 / var(response) )

The symmetric matrix should have as many rows and columns as the number of levels in the factor 'x'. The values in the matrix define the initial values of the variance-covariance components that will be subject to the constraints provided in thetaC. If not provided, initial values will be calculated as:

diag(ncol(mm))*.05 + matrix(.1,ncol(mm),ncol(mm))

where mm is the incidence matrix for the factor 'x'.

Author

Giovanny Covarrubias-Pazaran

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

See Also

See the function vsc to know how to use dsc in the mmec solver.

Examples

x <- as.factor(c(1:5,1:5,1:5));x
dsc(x)
## how to use the theta and thetaC arguments:
# data(DT_example)
# DT <- DT_example
# theta <- diag(3)*2; theta # initial VCs
# thetaC <- diag(3)*3; thetaC # fixed VCs
# ans1 <- mmec(Yield~Env,
#              random= ~ vsc( dsc(Env,theta = theta,thetaC = thetaC),isc(Name) ),
#              rcov= ~ units,
#              data=DT)
# summary(ans1)$varcomp