vs: variance structure

Description

`vs` is the main function to create the variance-covariance structure for the random effect to be fitted in the mmer solver.

Usage

vs(..., Gu=NULL, Gt=NULL, Gtc=NULL)

Arguments

...

unknown variance-covariance structures (i.e. us,ds,at,cs) and the random effect where such covariance structure will be used (the random effect of interest).

the known variance-covariance structure for the random effect (i.e. relationship matrix among individuals or any other known covariance structure). If NULL, then an identity matrix is assumed.

matrix with initial values of the variance-covariance components for the random effect. If NULL the program will provide these.

Gtc

matrix of constraints for the variance-covariance components for the random effect (Gt) according to the following rules:

0: not to be estimated

1: estimated and constrained to be positive

2: estimated and unconstrained

3: not to be estimated but fixed value provided in Gt

Value

$res: a list with all neccesary elements (incidence matrices, known var-cov structures, unknown covariance structures to be estimated and constraints) to be used in the mmer solver.

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

Examples

Run this code

# NOT RUN {
data(DT_example)
head(DT)

## ============================ ##
## example to use ds() structure (DIAGONAL)
## ============================ ##
ds(DT$Year)
mix <- mmer(Yield~Env, 
            random= ~ vs(ds(Year),Name),
            rcov=~ vs(ds(Year),units),
            data=DT)

## ============================ ##
## example to use at() structure (level-specific)
## ============================ ##
unique(DT$Year)
mix <- mmer(Yield~Env, 
            random= ~ vs(at(Year,c("2011","2012")),Name),
            rcov=~ vs(ds(Year),units),
            data=DT)

## ============================ ##
## example to use us() structure (UNSTRUCTURED)
## ============================ ##
us(DT$Year)
mix <- mmer(Yield~Env, 
            random= ~ vs(us(Year),Name),
            rcov=~ vs(ds(Year),units),
            data=DT)

## ============================ ##
## example to use cs() structure (CUSTOMIZED)
## ============================ ##
unique(DT$Year)
mm <- matrix(1,3,3); mm[1,3] <- mm[3,1] <- 0;mm #don't estimate cov 2011-2013
mix <- mmer(Yield~Env, 
            random= ~ vs(cs(Year,mm),Name),
            rcov=~ vs(ds(Year),units),
            data=DT)

## ============================ ##
## example to use overlay() + vs() structure
## ============================ ##
data("DT_halfdiallel")
head(DT)
DT$femalef <- as.factor(DT$female)
DT$malef <- as.factor(DT$male)
DT$genof <- as.factor(DT$geno)
A <- diag(7); colnames(A) <- rownames(A) <- 1:7;A # if you want to provide a covariance matrix
#### model using overlay
modh <- mmer(sugar~1, 
             random=~vs(overlay(femalef,malef), Gu=A) 
                     + genof,
              data=DT)

## ============================ ##
## example to use spl2D() + vs() structure
## ============================ ##            
# ### mimic two fields
# aa <- DT; bb <- DT
# aa$FIELD <- "A";bb$FIELD <- "B"
# set.seed(1234)
# aa$Yield <- aa$Yield + rnorm(length(aa$Yield),0,4)
# DT2 <- rbind(aa,bb)
# head(DT2)
# 
# mix <- mmer(Yield~1,
#             random=~vs(ds(FIELD),id, Gu=A) + 
#               vs(ds(FIELD),Rowf) +
#               vs(ds(FIELD),Colf) +
#                 vs(ds(FIELD),spl2D(Row,Col)),
#             rcov=~vs(ds(FIELD),units),
#             data=DT2)

# }