Simulate under the liability threshold model (multiple phenotypes).
Source:R/Simulate_under_LTM.R
simulate_under_LTM_multi.Rdsimulate_under_LTM_multi simulates families and thresholds under
the liability threshold model for a given family structure and multiple
phenotypes. Please note that it is not possible to simulate different
family structures.
Arguments
- fam_vec
A vector of strings holding the different family members. All family members must be represented by strings from the following list:
m(Mother)f(Father)c[0-9]*.[0-9]*(Children)mgm(Maternal grandmother)mgf(Maternal grandfather)pgm(Paternal grandmother)pgf(Paternal grandfather)s[0-9]*(Full siblings)mhs[0-9]*(Half-siblings - maternal side)phs[0-9]*(Half-siblings - paternal side)mau[0-9]*(Aunts/Uncles - maternal side)pau[0-9]*(Aunts/Uncles - paternal side). Defaults toc("m","f","s1","mgm","mgf","pgm","pgf").
- n_fam
A named vector holding the desired number of family members. See
setNames. All names must be picked from the list mentioned above. Defaults toNULL.- add_ind
A logical scalar indicating whether the genetic component of the full liability as well as the full liability for the underlying target individual should be included in the covariance matrix. Defaults to
TRUE.- genetic_corrmat
A numeric matrix holding the genetic correlations between the desired phenotypes. All diagonal entries must be equal to one, while all off-diagonal entries must be between -1 and 1. In addition, the matrix must be symmetric. Defaults to
diag(3).- full_corrmat
A numeric matrix holding the full correlations between the desired phenotypes. All diagonal entries must be equal to one, while all off-diagonal entries must be between -1 and 1. In addition, the matrix must be symmetric. Defaults to
diag(3).- h2_vec
A numeric vector holding the liability-scale heritabilities for a number of phenotype. All entries must be non-negative. Note that under the liability threshold model, the heritabilities must also be at most 1. Defaults to
rep(0.5,3).- phen_names
A character vector holding the phenotype names. These names will be used to create the row and column names for the covariance matrix. If it is not specified, the names will default to phenotype1, phenotype2, etc. Defaults to
NULL.- n_sim
A positive number representing the number of simulations. Defaults to 1000.
- pop_prev
A numeric vector holding the population prevalences, i.e. the overall prevalences in the population. All entries in
pop_prevmust be positive and smaller than 1. Defaults torep(.1,3).
Value
If either fam_vec or n_fam is used as the argument and if it is of the
required format, if genetic_corrmat and full_corrmat are two numeric
and symmetric matrices satisfying that all diagonal entries are one and that all
off-diagonal entries are between -1 and 1, if the liability-scale heritabilities in
h2_vec are numbers satisfying \(0 \leq h^2_i\) for all \(i \in \{1,...,n_pheno\}\),
n_sim is a strictly positive number, and pop_prev is a positive numeric
vector such that all entries are at most one,
then the output will be a list containing lists for each phenotype.
The first outer list, which is named after the first phenotype in phen_names,
holds the tibble sim_obs, which holds the simulated liabilities, the
disease status and the current age/age-of-onset for all family members in each of
the n_sim families for the first phenotype.
As the first outer list, the second outer list, which is named after the second
phenotype in phen_names, holds the tibble sim_obs, which holds
the simulated liabilities, the disease status and the current age/age-of-onset
for all family members in each of the n_sim families for the second phenotype.
There is a list containing sim_obs for each phenotype in phen_names.
The last list entry, thresholds, holds the family identifier, the personal
identifier, the role (specified in fam_vec or n_fam) as well as the lower and
upper thresholds for all individuals in all families and all phenotypes.
Note that this tibble has the format required in estimate_liability.
Finally, note that if neither fam_vec nor n_fam are specified, the function
returns the disease status, the current age/age-of-onset, the lower and upper
thresholds, as well as the personal identifier for a single individual, namely
the individual under consideration (called o).
If both fam_vec and n_fam are defined, the user is asked to '
decide on which of the two vectors to use.
Examples
simulate_under_LTM_multi()
#> $phenotype1
#> $phenotype1$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype1 o_phenotype1 m_phenotype1 f_phenotype1 s1_phenotype1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 0.959 1.34 1.38 0.820 2.04
#> 2 fam_ID_2 1.46 2.33 -0.548 1.43 2.03
#> 3 fam_ID_3 -0.573 0.0225 -0.481 1.46 -0.288
#> 4 fam_ID_4 -0.104 -0.343 0.848 -0.355 0.226
#> 5 fam_ID_5 0.603 -0.165 0.287 -0.640 -1.40
#> 6 fam_ID_6 0.538 0.910 0.830 -1.45 0.693
#> 7 fam_ID_7 -0.0690 -0.425 -0.0800 0.844 0.250
#> 8 fam_ID_8 -0.175 0.131 -0.885 -0.675 -0.207
#> 9 fam_ID_9 -0.718 -0.706 -0.377 0.495 -0.166
#> 10 fam_ID_10 0.379 0.373 0.978 -0.412 0.384
#> # ℹ 990 more rows
#> # ℹ 20 more variables: mgm_phenotype1 <dbl>, mgf_phenotype1 <dbl>,
#> # pgm_phenotype1 <dbl>, pgf_phenotype1 <dbl>, o_phenotype1_status <lgl>,
#> # m_phenotype1_status <lgl>, f_phenotype1_status <lgl>,
#> # s1_phenotype1_status <lgl>, mgm_phenotype1_status <lgl>,
#> # mgf_phenotype1_status <lgl>, pgm_phenotype1_status <lgl>,
#> # pgf_phenotype1_status <lgl>, o_phenotype1_aoo <dbl>, …
#>
#>
#> $phenotype2
#> $phenotype2$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype2 o_phenotype2 m_phenotype2 f_phenotype2 s1_phenotype2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 -1.43 -0.746 -1.10 -0.444 -1.49
#> 2 fam_ID_2 -0.428 -1.34 -0.0952 -0.791 1.40
#> 3 fam_ID_3 -0.301 -0.645 1.71 -1.28 0.258
#> 4 fam_ID_4 -1.11 -2.16 -0.869 -2.03 -1.93
#> 5 fam_ID_5 0.424 1.10 0.922 0.871 -0.895
#> 6 fam_ID_6 0.114 -1.16 1.10 -1.62 -0.263
#> 7 fam_ID_7 0.993 1.45 1.31 0.0951 -0.787
#> 8 fam_ID_8 0.434 1.04 -0.585 2.83 1.05
#> 9 fam_ID_9 -0.174 -0.00258 -1.58 -1.02 -0.461
#> 10 fam_ID_10 0.424 0.970 1.06 -0.963 1.69
#> # ℹ 990 more rows
#> # ℹ 20 more variables: mgm_phenotype2 <dbl>, mgf_phenotype2 <dbl>,
#> # pgm_phenotype2 <dbl>, pgf_phenotype2 <dbl>, o_phenotype2_status <lgl>,
#> # m_phenotype2_status <lgl>, f_phenotype2_status <lgl>,
#> # s1_phenotype2_status <lgl>, mgm_phenotype2_status <lgl>,
#> # mgf_phenotype2_status <lgl>, pgm_phenotype2_status <lgl>,
#> # pgf_phenotype2_status <lgl>, o_phenotype2_aoo <dbl>, …
#>
#>
#> $phenotype3
#> $phenotype3$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype3 o_phenotype3 m_phenotype3 f_phenotype3 s1_phenotype3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 -0.0725 -1.13 -0.784 -0.972 0.0342
#> 2 fam_ID_2 0.313 0.00468 0.990 -1.18 0.570
#> 3 fam_ID_3 -0.510 -0.928 0.570 -0.349 -0.0342
#> 4 fam_ID_4 0.0908 -0.0394 1.63 0.305 2.16
#> 5 fam_ID_5 -0.941 -1.83 0.512 0.956 -0.377
#> 6 fam_ID_6 -0.980 -0.356 -1.78 -0.548 -1.06
#> 7 fam_ID_7 -0.723 -0.798 -1.35 -0.793 -0.999
#> 8 fam_ID_8 -0.475 -1.20 0.422 -1.64 0.597
#> 9 fam_ID_9 0.655 -0.592 1.30 -1.26 -0.836
#> 10 fam_ID_10 -1.02 -0.0343 0.926 -0.677 -0.893
#> # ℹ 990 more rows
#> # ℹ 20 more variables: mgm_phenotype3 <dbl>, mgf_phenotype3 <dbl>,
#> # pgm_phenotype3 <dbl>, pgf_phenotype3 <dbl>, o_phenotype3_status <lgl>,
#> # m_phenotype3_status <lgl>, f_phenotype3_status <lgl>,
#> # s1_phenotype3_status <lgl>, mgm_phenotype3_status <lgl>,
#> # mgf_phenotype3_status <lgl>, pgm_phenotype3_status <lgl>,
#> # pgf_phenotype3_status <lgl>, o_phenotype3_aoo <dbl>, …
#>
#>
#> $thresholds
#> # A tibble: 8,000 × 9
#> fam_ID indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o 1.34 1.34 -Inf
#> 2 fam_ID_2 fam_ID_2_1 o 2.34 2.34 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.72 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.21 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.99 -Inf
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 2.59 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 3.21 1.45
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 2.95 -Inf
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 2.43 -Inf
#> 10 fam_ID_10 fam_ID_10… o -Inf 2.79 -Inf
#> # ℹ 7,990 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>
genetic_corrmat <- matrix(0.4, 3, 3)
diag(genetic_corrmat) <- 1
full_corrmat <- matrix(0.6, 3, 3)
diag(full_corrmat) <- 1
simulate_under_LTM_multi(fam_vec = NULL, n_fam = stats::setNames(c(1,1,1,2,2),
c("m","mgm","mgf","s","mhs")))
#> $phenotype1
#> $phenotype1$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype1 o_phenotype1 m_phenotype1 mgm_phenotype1 mgf_phenotype1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 0.364 1.27 0.731 0.919 -0.485
#> 2 fam_ID_2 -1.04 -1.87 -1.70 -0.353 -0.990
#> 3 fam_ID_3 -0.634 -1.41 -0.667 -0.892 -1.71
#> 4 fam_ID_4 -0.216 0.203 -0.102 -0.195 0.679
#> 5 fam_ID_5 0.0215 -0.109 -0.187 -0.465 0.611
#> 6 fam_ID_6 0.477 -0.552 -0.365 1.49 0.318
#> 7 fam_ID_7 0.402 -0.265 -0.267 -0.703 0.211
#> 8 fam_ID_8 -0.489 -0.622 -0.882 -0.0498 -0.670
#> 9 fam_ID_9 1.00 1.17 0.394 -0.555 1.25
#> 10 fam_ID_… -0.210 0.405 0.512 -1.04 -0.327
#> # ℹ 990 more rows
#> # ℹ 20 more variables: s1_phenotype1 <dbl>, s2_phenotype1 <dbl>,
#> # mhs1_phenotype1 <dbl>, mhs2_phenotype1 <dbl>, o_phenotype1_status <lgl>,
#> # m_phenotype1_status <lgl>, mgm_phenotype1_status <lgl>,
#> # mgf_phenotype1_status <lgl>, s1_phenotype1_status <lgl>,
#> # s2_phenotype1_status <lgl>, mhs1_phenotype1_status <lgl>,
#> # mhs2_phenotype1_status <lgl>, o_phenotype1_aoo <dbl>, …
#>
#>
#> $phenotype2
#> $phenotype2$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype2 o_phenotype2 m_phenotype2 mgm_phenotype2 mgf_phenotype2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 -0.0964 -0.290 -0.220 -0.820 -0.259
#> 2 fam_ID_2 -0.0480 -0.0149 0.625 0.260 1.07
#> 3 fam_ID_3 -0.406 0.941 0.587 -0.670 -0.0582
#> 4 fam_ID_4 -0.363 -0.338 1.69 1.01 0.137
#> 5 fam_ID_5 0.908 1.35 0.792 0.461 -0.771
#> 6 fam_ID_6 0.315 -0.0514 -0.552 -0.328 0.288
#> 7 fam_ID_7 -0.950 -0.129 0.515 1.33 1.85
#> 8 fam_ID_8 -0.0159 -0.567 1.02 -0.513 -1.85
#> 9 fam_ID_9 -0.581 0.112 0.142 -0.528 1.68
#> 10 fam_ID_… 0.770 0.633 1.53 1.66 0.574
#> # ℹ 990 more rows
#> # ℹ 20 more variables: s1_phenotype2 <dbl>, s2_phenotype2 <dbl>,
#> # mhs1_phenotype2 <dbl>, mhs2_phenotype2 <dbl>, o_phenotype2_status <lgl>,
#> # m_phenotype2_status <lgl>, mgm_phenotype2_status <lgl>,
#> # mgf_phenotype2_status <lgl>, s1_phenotype2_status <lgl>,
#> # s2_phenotype2_status <lgl>, mhs1_phenotype2_status <lgl>,
#> # mhs2_phenotype2_status <lgl>, o_phenotype2_aoo <dbl>, …
#>
#>
#> $phenotype3
#> $phenotype3$sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g_phenotype3 o_phenotype3 m_phenotype3 mgm_phenotype3 mgf_phenotype3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 -0.0144 -0.0573 0.130 0.0207 -0.862
#> 2 fam_ID_2 0.105 -0.232 1.72 0.366 1.80
#> 3 fam_ID_3 0.126 0.322 -0.349 0.860 -0.0310
#> 4 fam_ID_4 0.667 -0.0342 1.57 -1.03 0.448
#> 5 fam_ID_5 1.08 1.33 0.936 -0.214 0.634
#> 6 fam_ID_6 0.492 0.934 1.08 0.204 0.823
#> 7 fam_ID_7 0.560 1.76 -0.474 -0.230 -0.250
#> 8 fam_ID_8 0.501 0.737 0.0492 0.539 0.808
#> 9 fam_ID_9 1.43 1.98 2.74 2.63 -0.841
#> 10 fam_ID_… 0.228 0.624 0.524 0.944 -0.0716
#> # ℹ 990 more rows
#> # ℹ 20 more variables: s1_phenotype3 <dbl>, s2_phenotype3 <dbl>,
#> # mhs1_phenotype3 <dbl>, mhs2_phenotype3 <dbl>, o_phenotype3_status <lgl>,
#> # m_phenotype3_status <lgl>, mgm_phenotype3_status <lgl>,
#> # mgf_phenotype3_status <lgl>, s1_phenotype3_status <lgl>,
#> # s2_phenotype3_status <lgl>, mhs1_phenotype3_status <lgl>,
#> # mhs2_phenotype3_status <lgl>, o_phenotype3_aoo <dbl>, …
#>
#>
#> $thresholds
#> # A tibble: 8,000 × 9
#> fam_ID indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o -Inf 2.87 -Inf
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.87 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 3.31 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 2.43 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.59 1.35
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 3.03 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 3.21 -Inf
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 2.79 -Inf
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 3.48 -Inf
#> 10 fam_ID_10 fam_ID_10… o -Inf 2.51 -Inf
#> # ℹ 7,990 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>
simulate_under_LTM_multi(fam_vec = c("m","f","s1"), add_ind = FALSE,
genetic_corrmat = genetic_corrmat, full_corrmat = full_corrmat, n_sim = 100)
#> $phenotype1
#> $phenotype1$sim_obs
#> # A tibble: 100 × 10
#> fam_ID m_phenotype1 f_phenotype1 s1_phenotype1 m_phenotype1_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fam_ID_1 1.29 -0.656 -0.519 TRUE
#> 2 fam_ID_2 -1.31 0.225 -0.430 FALSE
#> 3 fam_ID_3 0.187 0.435 0.220 FALSE
#> 4 fam_ID_4 0.0120 0.637 -0.231 FALSE
#> 5 fam_ID_5 0.199 1.46 -0.779 FALSE
#> 6 fam_ID_6 1.04 -1.28 -0.761 FALSE
#> 7 fam_ID_7 -0.233 0.581 0.365 FALSE
#> 8 fam_ID_8 -2.32 0.0143 -0.768 FALSE
#> 9 fam_ID_9 -0.659 -0.0169 -0.0988 FALSE
#> 10 fam_ID_10 -0.765 -0.0794 -1.73 FALSE
#> # ℹ 90 more rows
#> # ℹ 5 more variables: f_phenotype1_status <lgl>, s1_phenotype1_status <lgl>,
#> # m_phenotype1_aoo <dbl>, f_phenotype1_aoo <dbl>, s1_phenotype1_aoo <dbl>
#>
#>
#> $phenotype2
#> $phenotype2$sim_obs
#> # A tibble: 100 × 10
#> fam_ID m_phenotype2 f_phenotype2 s1_phenotype2 m_phenotype2_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fam_ID_1 0.491 -1.03 0.418 FALSE
#> 2 fam_ID_2 -1.00 0.141 -0.605 FALSE
#> 3 fam_ID_3 0.991 1.35 0.787 FALSE
#> 4 fam_ID_4 0.306 0.373 -0.652 FALSE
#> 5 fam_ID_5 0.0785 1.52 -0.0798 FALSE
#> 6 fam_ID_6 -0.665 -0.928 -0.751 FALSE
#> 7 fam_ID_7 -0.587 0.732 -0.109 FALSE
#> 8 fam_ID_8 1.08 -0.769 -0.565 FALSE
#> 9 fam_ID_9 -1.52 0.369 0.487 FALSE
#> 10 fam_ID_10 -0.788 0.773 -0.322 FALSE
#> # ℹ 90 more rows
#> # ℹ 5 more variables: f_phenotype2_status <lgl>, s1_phenotype2_status <lgl>,
#> # m_phenotype2_aoo <dbl>, f_phenotype2_aoo <dbl>, s1_phenotype2_aoo <dbl>
#>
#>
#> $phenotype3
#> $phenotype3$sim_obs
#> # A tibble: 100 × 10
#> fam_ID m_phenotype3 f_phenotype3 s1_phenotype3 m_phenotype3_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fam_ID_1 0.0131 -0.574 0.137 FALSE
#> 2 fam_ID_2 -0.387 0.634 0.0247 FALSE
#> 3 fam_ID_3 2.01 -0.632 1.24 TRUE
#> 4 fam_ID_4 -1.67 1.37 -1.10 FALSE
#> 5 fam_ID_5 2.31 0.246 1.48 TRUE
#> 6 fam_ID_6 0.816 -1.32 -1.25 FALSE
#> 7 fam_ID_7 -0.326 0.230 -0.565 FALSE
#> 8 fam_ID_8 -0.900 -0.895 -1.03 FALSE
#> 9 fam_ID_9 -0.689 0.190 -1.16 FALSE
#> 10 fam_ID_10 -2.11 -0.136 -1.80 FALSE
#> # ℹ 90 more rows
#> # ℹ 5 more variables: f_phenotype3_status <lgl>, s1_phenotype3_status <lgl>,
#> # m_phenotype3_aoo <dbl>, f_phenotype3_aoo <dbl>, s1_phenotype3_aoo <dbl>
#>
#>
#> $thresholds
#> # A tibble: 300 × 9
#> fam_ID indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 m 1.29 1.29 -Inf
#> 2 fam_ID_2 fam_ID_2_1 m -Inf 1.64 -Inf
#> 3 fam_ID_3 fam_ID_3_1 m -Inf 1.45 -Inf
#> 4 fam_ID_4 fam_ID_4_1 m -Inf 2.13 -Inf
#> 5 fam_ID_5 fam_ID_5_1 m -Inf 2.18 -Inf
#> 6 fam_ID_6 fam_ID_6_1 m -Inf 1.44 -Inf
#> 7 fam_ID_7 fam_ID_7_1 m -Inf 1.54 -Inf
#> 8 fam_ID_8 fam_ID_8_1 m -Inf 2.26 -Inf
#> 9 fam_ID_9 fam_ID_9_1 m -Inf 1.81 -Inf
#> 10 fam_ID_10 fam_ID_10… m -Inf 2.22 -Inf
#> # ℹ 290 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>
simulate_under_LTM_multi(fam_vec = c(), n_fam = NULL, add_ind = TRUE, n_sim = 150)
#> $phenotype1
#> $phenotype1$sim_obs
#> # A tibble: 150 × 5
#> fam_ID g_phenotype1 o_phenotype1 o_phenotype1_status o_phenotype1_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fam_ID_1 -0.437 -0.0939 FALSE 15
#> 2 fam_ID_2 -0.898 -0.937 FALSE 27
#> 3 fam_ID_3 -0.144 -0.293 FALSE 36
#> 4 fam_ID_4 -0.492 -2.29 FALSE 34
#> 5 fam_ID_5 1.21 -0.194 FALSE 35
#> 6 fam_ID_6 -0.331 -0.384 FALSE 22
#> 7 fam_ID_7 1.62 1.09 FALSE 24
#> 8 fam_ID_8 0.377 -0.485 FALSE 12
#> 9 fam_ID_9 -0.0210 -0.167 FALSE 15
#> 10 fam_ID_10 0.0714 -0.639 FALSE 30
#> # ℹ 140 more rows
#>
#>
#> $phenotype2
#> $phenotype2$sim_obs
#> # A tibble: 150 × 5
#> fam_ID g_phenotype2 o_phenotype2 o_phenotype2_status o_phenotype2_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fam_ID_1 0.103 -0.0641 FALSE 15
#> 2 fam_ID_2 0.523 0.384 FALSE 27
#> 3 fam_ID_3 -0.482 -0.886 FALSE 36
#> 4 fam_ID_4 0.565 1.10 FALSE 34
#> 5 fam_ID_5 0.271 -0.645 FALSE 35
#> 6 fam_ID_6 -0.946 -0.905 FALSE 22
#> 7 fam_ID_7 -0.0989 -0.0752 FALSE 24
#> 8 fam_ID_8 0.802 1.78 TRUE 56
#> 9 fam_ID_9 -0.201 -0.538 FALSE 15
#> 10 fam_ID_10 -0.452 0.795 FALSE 30
#> # ℹ 140 more rows
#>
#>
#> $phenotype3
#> $phenotype3$sim_obs
#> # A tibble: 150 × 5
#> fam_ID g_phenotype3 o_phenotype3 o_phenotype3_status o_phenotype3_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fam_ID_1 -0.356 -0.0968 FALSE 15
#> 2 fam_ID_2 -0.278 -1.13 FALSE 27
#> 3 fam_ID_3 1.12 1.96 TRUE 51
#> 4 fam_ID_4 1.11 0.524 FALSE 34
#> 5 fam_ID_5 0.379 0.772 FALSE 35
#> 6 fam_ID_6 0.0283 -0.00122 FALSE 22
#> 7 fam_ID_7 0.248 0.807 FALSE 24
#> 8 fam_ID_8 0.117 -1.22 FALSE 12
#> 9 fam_ID_9 -1.12 -0.290 FALSE 15
#> 10 fam_ID_10 -0.599 -0.880 FALSE 30
#> # ℹ 140 more rows
#>
#>
#> $thresholds
#> # A tibble: 150 × 9
#> fam_ID indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o -Inf 3.38 -Inf
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.95 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.59 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 2.68 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.63 -Inf
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 3.14 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 3.06 -Inf
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 3.48 1.78
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 3.38 -Inf
#> 10 fam_ID_10 fam_ID_10… o -Inf 2.83 -Inf
#> # ℹ 140 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>