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
## 1  0.80376835  1           1.933232           1.132927 -1.129464e+00
## 2  0.11432330  1           1.933232           1.132927 -1.818909e+00
## 3  2.04598297  1           1.933232           1.132927  1.127508e-01
## 4  1.93333124  1           1.933232           1.132927  9.902713e-05
## 5  1.95105609  1           1.933232           1.132927  1.782388e-02
## 6  2.56116516  1           1.933232           1.132927  6.279329e-01
## 7  3.32733216  1           1.933232           1.132927  1.394100e+00
## 8  1.47309415  1           1.933232           1.132927 -4.601381e-01
## 9  0.86767664  1           1.933232           1.132927 -1.065556e+00
## 10 2.58031042  1           1.933232           1.132927  6.470782e-01
## 11 2.71157185  1           1.933232           1.132927  7.783396e-01
## 12 0.42173186  1           1.933232           1.132927 -1.511500e+00
## 13 1.65539512  1           1.933232           1.132927 -2.778371e-01
## 14 2.48173738  1           1.933232           1.132927  5.485052e-01
## 15 0.07352694  0           1.933232           1.132927 -1.859705e+00
## 16 0.27413919  1           1.933232           1.132927 -1.659093e+00
## 17 2.31435706  1           1.933232           1.132927  3.811248e-01
## 18 1.86984706  1           1.933232           1.132927 -6.338515e-02
## 19 2.01284413  0           1.933232           1.132927  7.961192e-02
## 20 2.26003535  1           1.933232           1.132927  3.268031e-01
##      residual2 individual squid_pop
## 1  -0.76705492          1         1
## 2  -0.66569912          1         1
## 3  -2.24392564          1         1
## 4   0.64642188          1         1
## 5  -0.17289871          1         1
## 6  -0.22721373          1         1
## 7   1.77025265          1         1
## 8  -0.06036272          1         1
## 9  -0.13107580          1         1
## 10  0.51965440          1         1
## 11 -0.94775391          1         1
## 12  0.21561112          1         1
## 13 -0.80144601          1         1
## 14 -0.76227263          1         1
## 15 -0.63847377          1         1
## 16 -2.33136433          1         1
## 17  0.10744440          1         1
## 18  1.32416729          1         1
## 19 -0.57956449          1         1
## 20 -1.10879960          1         1

data <- get_population_data(squid_data)