4.3 Sex specific genetic variance and inter-sexual genetic correlations

ds <- data.frame(animal=BTped[,"animal"],sex=sample(c("Female","Male"),nrow(BTped), replace=TRUE))

squid_data <- simulate_population(
  parameters = list(
    sex=list(
      fixed=TRUE,
      names=c("Female","Male"),
      beta=c(-0.5,0.5)
    ),
    animal= list(
      names = c("G_female","G_male"),
      vcov =matrix(c(0.1,-0.1,-0.1,0.4), nrow=2, ncol=2 ,byrow=TRUE)
      ),
    residual = list(
      names="residual",
      vcov = 0.1
    )
  ),
  data_structure = ds,
  pedigree = list(animal=BTped),
  model = "y = Female + Male + I(Female)*G_female + I(Male)*G_male + residual"
)

data <- get_population_data(squid_data)
head(data)

##            y Female Male    G_female      G_male    residual  animal    sex
## 1 -0.2529123      1    0 -0.03100451 -0.04841182  0.27809218 R187557 Female
## 2 -0.3205604      0    1 -0.33966106 -0.66849369 -0.15206674 R187559   Male
## 3  0.1583027      0    1  0.23986200 -0.26773808 -0.07395921 R187568   Male
## 4  0.9643963      0    1 -0.10503600  0.41074154  0.05365477 R187518   Male
## 5  0.5121345      0    1  0.31946969 -0.11387147  0.12600596 R187528   Male
## 6  1.2310101      0    1  0.11330433  0.47106512  0.25994496 R187945   Male
##   squid_pop
## 1         1
## 2         1
## 3         1
## 4         1
## 5         1
## 6         1

par(mfrow=c(1,2))
boxplot(y~factor(sex),data)
plot(G_female~G_male,data)