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 dimensions t x t (t equal to number of traits) with initial values of the variance-covariance components for the random effect specified in the .... argument If NULL the program will provide these.

Gtc

matrix with dimensions t x t (t equal to number of traits) of constraints for the variance-covariance components for the random effect specified in the ... argument according to the following rules:

0: not to be estimated

1: estimated and constrained to be positive (i.e. variance component)

2: estimated and unconstrained (can be negative or positive, i.e. covariance component)

3: not to be estimated but fixed (value has to be provided in the Gt argument)

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)

# }