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.
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 to c("m","f","s1","mgm","mgf","pgm","pgf").
A named vector holding the desired number of family members.
See setNames.
All names must be picked from the list mentioned above. Defaults to NULL.
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.
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).
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).
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).
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.
A positive number representing the number of simulations. Defaults to 1000.
A numeric vector holding the population prevalences, i.e. the
overall prevalences in the population. All entries in pop_prev must be positive
and smaller than 1. Defaults to rep(.1,3).
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.
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 0.295 -0.307 1.73 0.289 -0.367
#> 2 fam_ID_2 -0.987 0.222 0.0525 -2.26 -2.74
#> 3 fam_ID_3 0.456 -0.0926 -0.338 0.462 0.0830
#> 4 fam_ID_4 -0.0917 0.183 0.797 0.569 -0.186
#> 5 fam_ID_5 -1.18 -0.551 -0.522 -0.631 2.10
#> 6 fam_ID_6 0.0621 -0.204 -1.04 0.567 -0.618
#> 7 fam_ID_7 0.248 1.54 -0.613 0.539 0.131
#> 8 fam_ID_8 -0.581 -1.16 -1.01 -0.355 -0.937
#> 9 fam_ID_9 0.760 0.434 -0.246 0.957 1.34
#> 10 fam_ID_10 -0.131 -0.0710 0.339 -0.792 -1.28
#> # 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 -0.608 -0.855 -0.0746 -2.56 -0.760
#> 2 fam_ID_2 -0.0726 0.720 -1.03 -1.96 -1.66
#> 3 fam_ID_3 -0.726 -1.12 -0.151 -1.43 -1.08
#> 4 fam_ID_4 0.582 0.247 -0.762 -1.28 0.358
#> 5 fam_ID_5 1.06 1.76 1.07 0.289 1.56
#> 6 fam_ID_6 0.0155 -1.21 0.0204 -0.955 -0.966
#> 7 fam_ID_7 0.623 0.753 0.654 1.56 -0.918
#> 8 fam_ID_8 -0.612 -0.981 -0.648 2.31 -0.194
#> 9 fam_ID_9 0.603 1.04 0.911 0.567 1.07
#> 10 fam_ID_10 -0.954 -0.314 -1.53 -0.512 -0.566
#> # 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 0.413 0.931 0.325 0.508 -0.345
#> 2 fam_ID_2 -0.231 -0.594 1.29 0.130 0.926
#> 3 fam_ID_3 -0.573 -2.27 0.0160 0.140 0.267
#> 4 fam_ID_4 -1.17 -1.94 -1.98 -1.05 -0.553
#> 5 fam_ID_5 -0.255 -0.103 0.0213 -0.241 -0.503
#> 6 fam_ID_6 -0.699 -0.730 -1.91 -0.447 -1.48
#> 7 fam_ID_7 -0.138 -1.17 -0.940 0.491 0.323
#> 8 fam_ID_8 -0.823 -0.301 -1.28 1.10 -1.31
#> 9 fam_ID_9 -1.13 -0.891 -0.0317 -0.420 -0.0568
#> 10 fam_ID_10 -0.225 -0.906 -0.592 0.765 -0.610
#> # 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 -Inf 2.91 -Inf
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.76 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.72 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.06 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 3.52 1.74
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 2.72 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o 1.54 1.54 -Inf
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 2.63 -Inf
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 2.68 -Inf
#> 10 fam_ID_10 fam_ID_10… o -Inf 3.17 -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 -0.249 -0.898 0.339 0.749 0.346
#> 2 fam_ID_2 0.216 0.560 -1.01 1.38 -0.171
#> 3 fam_ID_3 -0.506 1.26 0.279 -0.394 -0.444
#> 4 fam_ID_4 0.0933 0.136 -0.776 -0.457 -0.367
#> 5 fam_ID_5 0.680 0.395 0.766 -0.163 -0.261
#> 6 fam_ID_6 0.0549 -0.495 -0.844 -0.615 0.529
#> 7 fam_ID_7 0.0417 0.580 -1.25 -1.08 -0.270
#> 8 fam_ID_8 -0.865 -1.23 0.158 0.562 0.213
#> 9 fam_ID_9 -0.233 -0.178 -0.876 -0.0426 -0.600
#> 10 fam_ID_… 0.723 0.605 -0.516 0.626 0.459
#> # 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 0.605 1.66 1.72 -0.332 1.52
#> 2 fam_ID_2 0.992 1.13 0.174 1.90 -1.08
#> 3 fam_ID_3 -0.151 -0.360 -1.08 -0.317 -0.140
#> 4 fam_ID_4 0.412 0.420 -0.0552 -2.48 -1.25
#> 5 fam_ID_5 -0.636 -1.30 -1.06 0.322 -0.386
#> 6 fam_ID_6 -1.59 -1.49 -1.15 -2.28 1.01
#> 7 fam_ID_7 -0.546 0.797 -1.03 -0.849 0.149
#> 8 fam_ID_8 0.661 1.26 0.274 0.262 -0.796
#> 9 fam_ID_9 0.613 1.37 1.94 2.24 0.292
#> 10 fam_ID_… 0.247 -0.0475 1.41 0.450 1.14
#> # 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 0.496 1.21 -0.875 -0.956 0.747
#> 2 fam_ID_2 -0.142 0.355 -0.286 1.30 -1.53
#> 3 fam_ID_3 0.320 -0.974 0.939 -0.180 -0.227
#> 4 fam_ID_4 -0.519 -0.793 -0.216 0.925 -0.401
#> 5 fam_ID_5 0.284 -0.104 0.838 -0.260 0.784
#> 6 fam_ID_6 0.876 0.0263 0.514 1.93 0.0894
#> 7 fam_ID_7 -0.175 -0.417 0.135 0.654 0.319
#> 8 fam_ID_8 -1.12 0.309 0.292 0.165 1.21
#> 9 fam_ID_9 -0.400 0.0362 -0.655 -0.560 -1.01
#> 10 fam_ID_… -0.934 -0.585 -0.577 -1.57 -0.309
#> # 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 3.52 1.64
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.59 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.63 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.03 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.72 -Inf
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 2.91 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 2.99 -Inf
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 3.24 -Inf
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 3.21 1.37
#> 10 fam_ID_10 fam_ID_10… o -Inf 3.42 -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 0.229 -0.348 -1.11 FALSE
#> 2 fam_ID_2 -1.97 -0.241 0.0193 FALSE
#> 3 fam_ID_3 -0.509 1.12 0.868 FALSE
#> 4 fam_ID_4 0.743 1.64 0.915 FALSE
#> 5 fam_ID_5 0.750 1.31 -0.817 FALSE
#> 6 fam_ID_6 -0.817 -0.442 0.395 FALSE
#> 7 fam_ID_7 0.396 -0.739 -0.111 FALSE
#> 8 fam_ID_8 1.26 -1.27 0.645 FALSE
#> 9 fam_ID_9 -0.162 0.176 -0.927 FALSE
#> 10 fam_ID_10 0.362 0.0284 0.385 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 1.12 -1.37 0.155 FALSE
#> 2 fam_ID_2 -0.156 -0.643 -0.555 FALSE
#> 3 fam_ID_3 -2.86 -0.110 -0.212 FALSE
#> 4 fam_ID_4 0.410 2.72 0.191 FALSE
#> 5 fam_ID_5 0.706 0.632 -1.53 FALSE
#> 6 fam_ID_6 0.562 -0.500 0.467 FALSE
#> 7 fam_ID_7 -1.35 -1.22 -1.20 FALSE
#> 8 fam_ID_8 0.408 -0.691 0.0122 FALSE
#> 9 fam_ID_9 0.390 1.85 0.0707 FALSE
#> 10 fam_ID_10 1.82 -0.289 0.924 TRUE
#> # 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 -0.116 -0.931 -1.28 FALSE
#> 2 fam_ID_2 0.587 -0.272 -0.0452 FALSE
#> 3 fam_ID_3 -3.08 -0.174 -0.215 FALSE
#> 4 fam_ID_4 -1.12 1.63 -0.482 FALSE
#> 5 fam_ID_5 2.72 1.17 -0.268 TRUE
#> 6 fam_ID_6 1.15 -0.262 0.237 FALSE
#> 7 fam_ID_7 -0.148 -0.932 -0.337 FALSE
#> 8 fam_ID_8 0.697 -1.05 -1.06 FALSE
#> 9 fam_ID_9 -0.844 0.754 -1.19 FALSE
#> 10 fam_ID_10 -0.0166 0.446 0.564 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 -Inf 2.55 -Inf
#> 2 fam_ID_2 fam_ID_2_1 m -Inf 2.13 -Inf
#> 3 fam_ID_3 fam_ID_3_1 m -Inf 1.68 -Inf
#> 4 fam_ID_4 fam_ID_4_1 m -Inf 1.64 -Inf
#> 5 fam_ID_5 fam_ID_5_1 m -Inf 1.64 -Inf
#> 6 fam_ID_6 fam_ID_6_1 m -Inf 2.30 -Inf
#> 7 fam_ID_7 fam_ID_7_1 m -Inf 1.56 -Inf
#> 8 fam_ID_8 fam_ID_8_1 m -Inf 1.93 -Inf
#> 9 fam_ID_9 fam_ID_9_1 m -Inf 2.13 -Inf
#> 10 fam_ID_10 fam_ID_10… m -Inf 2.34 1.81
#> # 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)
#> Warning message:
#> Neither fam_vec nor n_fam is specified...
#> $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 1.33 2.61 TRUE 36
#> 2 fam_ID_2 0.123 -0.768 FALSE 38
#> 3 fam_ID_3 0.0207 1.16 FALSE 32
#> 4 fam_ID_4 -0.639 0.226 FALSE 25
#> 5 fam_ID_5 -1.16 -0.894 FALSE 32
#> 6 fam_ID_6 0.982 2.14 TRUE 47
#> 7 fam_ID_7 1.06 1.15 FALSE 13
#> 8 fam_ID_8 0.642 0.147 FALSE 12
#> 9 fam_ID_9 -0.832 -0.881 FALSE 30
#> 10 fam_ID_10 -1.15 -0.434 FALSE 13
#> # 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 -1.30 -2.11 FALSE 19
#> 2 fam_ID_2 0.730 0.613 FALSE 38
#> 3 fam_ID_3 -0.790 -1.24 FALSE 32
#> 4 fam_ID_4 1.30 0.955 FALSE 25
#> 5 fam_ID_5 0.0836 0.681 FALSE 32
#> 6 fam_ID_6 -0.0771 -1.20 FALSE 20
#> 7 fam_ID_7 0.167 0.622 FALSE 13
#> 8 fam_ID_8 -1.03 -1.56 FALSE 12
#> 9 fam_ID_9 -0.0209 0.699 FALSE 30
#> 10 fam_ID_10 -0.840 -0.399 FALSE 13
#> # 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 0.0704 0.704 FALSE 19
#> 2 fam_ID_2 0.769 0.546 FALSE 38
#> 3 fam_ID_3 0.303 1.08 FALSE 32
#> 4 fam_ID_4 -0.433 -0.904 FALSE 25
#> 5 fam_ID_5 0.0690 0.222 FALSE 32
#> 6 fam_ID_6 -0.390 0.386 FALSE 20
#> 7 fam_ID_7 0.0121 0.282 FALSE 13
#> 8 fam_ID_8 -1.06 0.185 FALSE 12
#> 9 fam_ID_9 -0.725 0.0824 FALSE 30
#> 10 fam_ID_10 0.864 1.79 TRUE 56
#> # 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 2.59 2.59 -Inf
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.51 -Inf
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.76 -Inf
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.03 -Inf
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.76 -Inf
#> 6 fam_ID_6 fam_ID_6_1 o 2.13 2.13 -Inf
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 3.45 -Inf
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 3.48 -Inf
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 2.83 -Inf
#> 10 fam_ID_10 fam_ID_10… o -Inf 3.45 -Inf
#> # ℹ 140 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>