3.3 Different distributions

individual <- list(
  vcov = matrix(c(
    1,0.5,
    0.5,1
    ),nrow=2,ncol=2,byrow=TRUE)
)

residual <- list(
  vcov = matrix(c(
    1,0.5,
    0.5,1
    ),nrow = 2,ncol = 2,byrow=TRUE),
  beta = matrix(c(
    1,0,
    0,0
    ),nrow = 2,ncol = 2,byrow=TRUE)
)


squid_data <- simulate_population(
  data_structure= make_structure(structure = "individual(100)",repeat_obs=20),
  n_response = 2,
  parameters=list(individual = individual, residual = residual),
  family=c("gaussian","binomial"), link=c("identity","logit")
)

data <- get_population_data(squid_data)
head(data,20)

##             y1 y2 individual_effect1 individual_effect2   residual1   residual2
## 1   0.71929104  0         -0.5257881          -1.042722  1.24507910  2.50335447
## 2   0.33325030  1         -0.5257881          -1.042722  0.85903836  1.44428517
## 3  -0.42718617  0         -0.5257881          -1.042722  0.09860189  1.26752630
## 4   0.44910069  0         -0.5257881          -1.042722  0.97488875  0.39978044
## 5  -0.62462727  0         -0.5257881          -1.042722 -0.09883921  0.73676976
## 6   0.12822742  0         -0.5257881          -1.042722  0.65401547  2.32798502
## 7  -0.10269091  0         -0.5257881          -1.042722  0.42309715  0.97088795
## 8  -1.16521217  0         -0.5257881          -1.042722 -0.63942411 -1.11697997
## 9  -2.02498920  0         -0.5257881          -1.042722 -1.49920114 -1.71662806
## 10  0.84702556  1         -0.5257881          -1.042722  1.37281362  0.31558774
## 11 -1.13218028  0         -0.5257881          -1.042722 -0.60639222  0.50095716
## 12 -1.01004461  0         -0.5257881          -1.042722 -0.48425655  0.92650060
## 13  0.71616352  0         -0.5257881          -1.042722  1.24195158 -1.25932342
## 14 -0.72239979  1         -0.5257881          -1.042722 -0.19661173 -0.51083678
## 15 -1.33642792  1         -0.5257881          -1.042722 -0.81063987 -1.35618916
## 16 -1.95735773  0         -0.5257881          -1.042722 -1.43156967 -1.13993143
## 17 -0.85504490  0         -0.5257881          -1.042722 -0.32925684  0.03603065
## 18  1.26558426  0         -0.5257881          -1.042722  1.79137232  2.14512837
## 19  0.04558226  0         -0.5257881          -1.042722  0.57137032  0.52313819
## 20  0.88752414  1         -0.5257881          -1.042722  1.41331220  0.87626903
##    individual squid_pop
## 1           1         1
## 2           1         1
## 3           1         1
## 4           1         1
## 5           1         1
## 6           1         1
## 7           1         1
## 8           1         1
## 9           1         1
## 10          1         1
## 11          1         1
## 12          1         1
## 13          1         1
## 14          1         1
## 15          1         1
## 16          1         1
## 17          1         1
## 18          1         1
## 19          1         1
## 20          1         1

data <- get_population_data(squid_data)