sommer (version 4.1.1)

fcm: fixed effect constraint indication matrix

Description

fcm creates a matrix with the correct number of columns to specify a constraint in the fixed effects using the Gtc argument of the vs function.

Usage

fcm(x, reps=NULL)

Arguments

x

vector of 1's and 0's corresponding to the traits for which this fixed effect should be fitted. For example, for a trivariate model if the fixed effect "x" wants to be fitted only for trait 1 and 2 but not for the 3rd trait then you would use fcm(c(1,1,0)) in the Gtc argument of the vs() function.

reps

integer specifying the number of times the matrix should be repeated in a list format to provide easily the constraints in complex models that use the ds(), us() or cs() structures.

Value

$res

a matrix or a list of matrices with the constraints to be provided in the Gtc argument of the vs function.

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

The function vs to know how to use fcm in the mmer solver.

Examples

# NOT RUN {
fcm(c(1,1,0))
fcm(c(0,1,1))
fcm(c(1,1,1))

fcm(c(1,1,1),2)

# ## model with Env estimated for both traits
# data(DT_example)
# DT <- DT_example
# A <- A_example
# ans4 <- mmer(cbind(Yield, Weight) ~ Env,
#               random= ~ vs(Name) + vs(Env:Name),
#               rcov= ~ vs(units),
#               data=DT)
# summary(ans4)$betas
# ## model with Env only estimated for Yield
# ans4b <- mmer(cbind(Yield, Weight) ~ vs(Env, Gtc=fcm(c(1,0))),
#              random= ~ vs(Name) + vs(Env:Name),
#              rcov= ~ vs(units),
#              data=DT)
# summary(ans4b)$betas

# }