inst/doc/v2.sommer.changes.and.faqs.R

In sommer: Solving Mixed Model Equations in R

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# iteration    LogLik     wall    cpu(sec)   restrained
#     1      -224.676   18:11:23      3           0
# Sistem is singular. Aborting the job. You could try a bigger tolparinv value.

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library(sommer)

data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
#### create the variance-covariance matrix
A <- A.mat(GT) # additive relationship matrix
#### look at the data and fit the model
set.seed(12)
DT2 <- droplevels(DT[sample(1:nrow(DT),100),]) # we simulate a dataset with only 100 animals
nrow(DT2); length(levels(DT2$id))

# we fit a model with the reduced datatset where only 100 blups will be returned since only
# 100 levels exist in the "id" column
mix1 <- mmer(Yield~1,
              random=~vsr(id,Gu=A)
                      + Rowf + Colf,
              rcov=~units,
              data=DT2, verbose = FALSE)
summary(mix1)
length(mix1$U$`u:id`$Yield) # only 100 levels

# we add additional levels to the "id" column and also provide them in the relationship matrix
levels(DT2$id) <- c(levels(DT2$id), setdiff(levels(DT$id), levels(DT2$id)))
mix2 <- mmer(Yield~1,
             random=~vsr(id,Gu=A)
             + Rowf + Colf,
             rcov=~units,
             data=DT2, verbose = FALSE)
summary(mix2)
length(mix2$U$`u:id`$Yield) # now 363 levels

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library(sommer)
data(DT_cpdata)
DT <- DT_cpdata
mix1 <- mmer(Yield~1,
              random=~ Rowf + Colf,
              rcov=~units,
              data=DT, verbose = FALSE)
summary(mix1)$varcomp

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library(sommer)
data(DT_cpdata)
DT <- DT_cpdata
mixAR1row <- mmer(Yield~1,
             random=~ vsr(Rowf, Gu=AR1(Rowf, rho=0.3)) + Colf,
             rcov=~units,
             data=DT, verbose = FALSE)
summary(mixAR1row)$varcomp

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library(sommer)
data(DT_cpdata)
DT <- DT_cpdata
mixAR1col <- mmer(Yield~1,
             random=~ Rowf + vsr(Colf, Gu=AR1(Colf, rho=0.3)),
             rcov=~units,
             data=DT, verbose = FALSE)
summary(mixAR1col)$varcomp

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library(sommer)
data(DT_cpdata)
DT <- DT_cpdata
mixAR1rowcol <- mmer(Yield~1,
                  random=~ vsr(Rowf:Colf, 
                              Gu=kronecker(AR1(Rowf, rho=0.3),AR1(Colf, rho=0.3),make.dimnames = TRUE) 
                              ),
                  rcov=~units,
                  data=DT, verbose = FALSE)
summary(mixAR1rowcol)$varcomp

## -----------------------------------------------------------------------------
data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
#### create the variance-covariance matrix
A <- A.mat(GT) # additive relationship matrix
#### look at the data and fit the model
mix1 <- mmer(Yield~1,
              random=~vsr(id,Gu=A),
              rcov=~units,
              data=DT, verbose = FALSE)
mix1$sigma$`u:id`

## -----------------------------------------------------------------------------
data(DT_cpdata)
DT <- DT_cpdata
GT <- GT_cpdata
MP <- MP_cpdata
#### create the variance-covariance matrix
A <- A.mat(GT) # additive relationship matrix
#### look at the data and fit the model
mix2 <- mmer(cbind(Yield,color)~1,
              random=~vsr(id,Gu=A, Gtc=unsm(2)),
              rcov=~vsr(units,Gtc=diag(2)),
              data=DT, verbose = FALSE)
mix2$sigma$`u:id`

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unsm(2)
mix2$sigma$`u:id`

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diag(2)
mix2$sigma$`u:units`

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mm <- matrix(3,1,1) ## matrix to fix the var comp
initialVal <- mix1$sigma_scaled$`u:id`/2 # we want to fix the vc to be half of the previous uinvariate model

mix3 <- mmer(Yield~1,
              random=~vsr(id, Gu=A, Gti=initialVal, Gtc=mm), # constrained
              rcov=~vsr(units), # unconstrained
              data=DT, verbose = FALSE)

# analyze variance components
summary(mix1)$varcomp
summary(mix3)$varcomp

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mm <- matrix(3,1,1) ## matrix to fix the var comp
vei <- var(DT$Yield, na.rm = TRUE) # we want to fix the vc to be half of the previous uinvariate model

mix3 <- mmec(Yield~1,
              random=~Rowf, # unconstrained
              rcov= ~ vsc(isc(units, thetaC=mm,theta=matrix(1/vei,1,1))), # constrained
              data=DT, verbose = FALSE)

# analyze variance components
summary(mix3)$varcomp

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library("MASS")  ## needed for mvrnorm
n <- 100
mu <- c(1,2)
Sigma <- matrix(c(10,5,5,10),2,2)
Y <- mvrnorm(n,mu,Sigma); colnames(Y) <- c("y1","y2")
## this simulates multivariate normal rvs
y <- as.vector(t(Y))
df1 <- data.frame(Y)
df2 <- data.frame(y)

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mix1 <- mmer(cbind(y1,y2)~1, rcov=~vsr(units, Gtc=unsm(2)), data=df1, verbose = FALSE)
mix1$sigma

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X <- kronecker(rep(1,n),diag(1,2))
V1 <- matrix(c(1,0,0,0),2,2)
V2 <- matrix(c(0,0,0,1),2,2)
V3 <- matrix(c(0,1,1,0),2,2)
sig1 <- kronecker(diag(1,n),V1) # variance component 1
sig2 <- kronecker(diag(1,n),V2) # variance component 2
gam <- kronecker(diag(1,n),V3) # covariance component
# now fit the model
mix2 <- mmer(y~X-1, rcov = ~vsr(sig1)+vsr(sig2)+vsr(gam,Gti = matrix(.15)), data=df2, verbose = FALSE)
mix2$sigmaVector

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sig <- sig1+sig2
mix3 <- mmer(y~X-1, rcov = ~vsr(sig)+vsr(gam,Gti = matrix(.15)), data=df2, nIters=30, verbose = FALSE)
mix3$sigmaVector