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
#> fid g_phenotype1 o_phenotype1 m_phenotype1 f_phenotype1 s1_phenotype1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 -0.753 -0.132 -1.09 -0.404 -0.270
#> 2 fid_2 -0.101 1.10 -0.767 -0.493 -1.14
#> 3 fid_3 -0.224 -0.891 -0.132 -1.38 -0.474
#> 4 fid_4 -0.0705 0.741 0.210 -0.378 0.641
#> 5 fid_5 0.738 0.133 -0.897 1.42 0.761
#> 6 fid_6 0.316 1.69 0.551 0.305 0.884
#> 7 fid_7 -0.245 -1.29 -1.64 -1.95 1.38
#> 8 fid_8 -0.321 -1.17 0.462 -0.611 1.99
#> 9 fid_9 0.687 0.892 0.365 -0.399 1.69
#> 10 fid_10 -0.696 -0.283 0.270 -1.24 -0.910
#> # 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
#> fid g_phenotype2 o_phenotype2 m_phenotype2 f_phenotype2 s1_phenotype2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 -0.229 -0.542 -0.266 2.28 0.714
#> 2 fid_2 0.327 -0.980 -0.829 -0.223 0.960
#> 3 fid_3 0.208 0.756 0.729 -0.376 -0.903
#> 4 fid_4 0.245 0.590 1.34 -0.538 0.334
#> 5 fid_5 0.766 1.07 -0.0648 0.988 -0.205
#> 6 fid_6 0.793 1.47 -0.964 -0.557 -0.409
#> 7 fid_7 -0.201 -0.486 0.192 1.28 1.16
#> 8 fid_8 0.631 0.920 0.134 -0.0381 1.23
#> 9 fid_9 -0.367 -0.522 1.10 1.73 0.481
#> 10 fid_10 1.21 0.744 0.900 0.299 1.33
#> # 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
#> fid g_phenotype3 o_phenotype3 m_phenotype3 f_phenotype3 s1_phenotype3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 -0.830 -1.66 1.40 1.33 0.971
#> 2 fid_2 -0.212 0.102 0.0530 -0.330 0.0383
#> 3 fid_3 -0.0909 -0.571 -0.155 0.461 0.476
#> 4 fid_4 -0.102 1.30 -1.62 0.817 -0.333
#> 5 fid_5 0.371 -1.33 1.01 -0.848 -0.658
#> 6 fid_6 0.413 1.28 -1.53 1.68 -0.715
#> 7 fid_7 0.998 0.270 0.848 0.725 1.85
#> 8 fid_8 -0.370 -0.859 -0.703 0.307 1.47
#> 9 fid_9 0.136 0.334 1.50 -0.395 0.841
#> 10 fid_10 -0.199 -0.630 0.192 -1.20 -1.20
#> # 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
#> fid indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fid_1 fid_1_1 o -Inf 2.83 -Inf
#> 2 fid_2 fid_2_1 o -Inf 2.95 -Inf
#> 3 fid_3 fid_3_1 o -Inf 2.76 -Inf
#> 4 fid_4 fid_4_1 o -Inf 3.38 -Inf
#> 5 fid_5 fid_5_1 o -Inf 3.42 -Inf
#> 6 fid_6 fid_6_1 o 1.68 1.68 1.47
#> 7 fid_7 fid_7_1 o -Inf 3.14 -Inf
#> 8 fid_8 fid_8_1 o -Inf 3.14 -Inf
#> 9 fid_9 fid_9_1 o -Inf 2.72 -Inf
#> 10 fid_10 fid_10_1 o -Inf 3.21 -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
#> fid g_phenotype1 o_phenotype1 m_phenotype1 mgm_phenotype1 mgf_phenotype1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 0.128 -1.17 0.822 -0.832 0.0560
#> 2 fid_2 -0.351 0.217 1.25 0.854 0.566
#> 3 fid_3 0.127 0.261 -1.31 -0.257 0.673
#> 4 fid_4 2.03 2.14 0.0462 0.890 1.77
#> 5 fid_5 -0.243 -0.248 2.01 -0.415 -1.36
#> 6 fid_6 -0.674 0.00275 -0.591 -0.674 1.10
#> 7 fid_7 -0.633 -1.05 -2.22 -0.151 -0.999
#> 8 fid_8 0.315 -0.0979 -0.278 1.45 0.0554
#> 9 fid_9 0.163 -0.744 -0.649 -0.468 0.626
#> 10 fid_10 0.269 0.728 -0.194 0.581 -0.280
#> # 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
#> fid g_phenotype2 o_phenotype2 m_phenotype2 mgm_phenotype2 mgf_phenotype2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 0.105 0.630 0.756 -0.228 0.665
#> 2 fid_2 0.896 1.75 1.12 1.28 0.490
#> 3 fid_3 -0.489 0.525 -0.688 -0.838 -0.273
#> 4 fid_4 0.554 -0.116 -1.61 -1.98 -1.82
#> 5 fid_5 -1.41 -1.50 -0.0545 1.14 -0.125
#> 6 fid_6 -0.525 0.716 0.354 0.886 0.977
#> 7 fid_7 0.0385 0.218 -1.19 0.340 0.875
#> 8 fid_8 -0.784 0.269 -0.858 -0.558 -0.798
#> 9 fid_9 0.126 0.453 -0.654 -0.870 0.173
#> 10 fid_10 0.289 0.484 0.425 -1.88 3.10
#> # 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
#> fid g_phenotype3 o_phenotype3 m_phenotype3 mgm_phenotype3 mgf_phenotype3
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fid_1 -0.353 0.292 -0.00167 -0.0630 0.497
#> 2 fid_2 -1.33 -1.45 0.0648 0.960 -1.07
#> 3 fid_3 -0.541 0.0715 0.574 0.225 0.979
#> 4 fid_4 1.11 1.60 0.720 -3.27 0.519
#> 5 fid_5 0.417 -1.21 -0.855 -0.712 0.170
#> 6 fid_6 0.802 0.997 1.14 0.604 0.721
#> 7 fid_7 0.554 0.772 1.31 0.0657 1.83
#> 8 fid_8 -0.615 -0.760 -1.43 -0.626 -1.47
#> 9 fid_9 0.432 0.0293 0.262 -0.331 -0.0442
#> 10 fid_10 -0.330 -0.336 0.509 0.474 -0.198
#> # 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
#> fid indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fid_1 fid_1_1 o -Inf 3.38 -Inf
#> 2 fid_2 fid_2_1 o -Inf 2.91 1.74
#> 3 fid_3 fid_3_1 o -Inf 2.79 -Inf
#> 4 fid_4 fid_4_1 o 2.13 2.13 -Inf
#> 5 fid_5 fid_5_1 o -Inf 3.24 -Inf
#> 6 fid_6 fid_6_1 o -Inf 3.31 -Inf
#> 7 fid_7 fid_7_1 o -Inf 2.91 -Inf
#> 8 fid_8 fid_8_1 o -Inf 2.79 -Inf
#> 9 fid_9 fid_9_1 o -Inf 3.17 -Inf
#> 10 fid_10 fid_10_1 o -Inf 3.24 -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
#> fid m_phenotype1 f_phenotype1 s1_phenotype1 m_phenotype1_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fid_1 -0.834 1.77 -0.887 FALSE
#> 2 fid_2 -0.993 0.682 -0.849 FALSE
#> 3 fid_3 -0.914 0.363 0.156 FALSE
#> 4 fid_4 -0.869 1.04 0.620 FALSE
#> 5 fid_5 1.82 -1.76 -0.339 TRUE
#> 6 fid_6 -0.523 -1.22 0.661 FALSE
#> 7 fid_7 1.69 0.0377 0.323 TRUE
#> 8 fid_8 0.519 -0.181 0.488 FALSE
#> 9 fid_9 -0.787 0.195 -0.0356 FALSE
#> 10 fid_10 -1.23 -1.43 -1.93 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
#> fid m_phenotype2 f_phenotype2 s1_phenotype2 m_phenotype2_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fid_1 0.841 1.27 0.330 FALSE
#> 2 fid_2 -2.21 -0.740 -1.61 FALSE
#> 3 fid_3 -0.585 -0.163 0.449 FALSE
#> 4 fid_4 0.706 -1.84 -1.17 FALSE
#> 5 fid_5 0.729 -1.46 1.13 FALSE
#> 6 fid_6 -0.264 -1.87 -0.432 FALSE
#> 7 fid_7 2.34 -0.131 0.490 TRUE
#> 8 fid_8 -0.125 -0.156 0.672 FALSE
#> 9 fid_9 0.316 -0.383 0.517 FALSE
#> 10 fid_10 -0.955 -1.40 -1.04 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
#> fid m_phenotype3 f_phenotype3 s1_phenotype3 m_phenotype3_status
#> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 fid_1 0.121 1.92 -0.906 FALSE
#> 2 fid_2 -0.299 0.266 -1.02 FALSE
#> 3 fid_3 -2.62 -0.952 -0.592 FALSE
#> 4 fid_4 0.172 -0.149 -1.03 FALSE
#> 5 fid_5 -0.238 -1.21 -0.407 FALSE
#> 6 fid_6 -0.342 -1.89 0.391 FALSE
#> 7 fid_7 -0.280 -0.702 -1.00 FALSE
#> 8 fid_8 0.115 -0.506 0.729 FALSE
#> 9 fid_9 1.24 0.919 0.515 FALSE
#> 10 fid_10 0.764 -0.642 -0.795 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
#> fid indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fid_1 fid_1_1 m -Inf 2.18 -Inf
#> 2 fid_2 fid_2_1 m -Inf 2.09 -Inf
#> 3 fid_3 fid_3_1 m -Inf 1.71 -Inf
#> 4 fid_4 fid_4_1 m -Inf 1.64 -Inf
#> 5 fid_5 fid_5_1 m 1.81 1.81 -Inf
#> 6 fid_6 fid_6_1 m -Inf 1.74 -Inf
#> 7 fid_7 fid_7_1 m 1.71 1.71 2.34
#> 8 fid_8 fid_8_1 m -Inf 2.01 -Inf
#> 9 fid_9 fid_9_1 m -Inf 1.68 -Inf
#> 10 fid_10 fid_10_1 m -Inf 1.62 -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
#> fid g_phenotype1 o_phenotype1 o_phenotype1_status o_phenotype1_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fid_1 -0.449 0.214 FALSE 37
#> 2 fid_2 0.161 0.600 FALSE 20
#> 3 fid_3 1.05 1.46 TRUE 68
#> 4 fid_4 0.751 -0.0429 FALSE 21
#> 5 fid_5 0.911 1.40 TRUE 72
#> 6 fid_6 -0.463 -2.00 FALSE 31
#> 7 fid_7 0.690 0.163 FALSE 26
#> 8 fid_8 -0.329 0.159 FALSE 34
#> 9 fid_9 -0.0705 -0.00849 FALSE 39
#> 10 fid_10 0.299 0.237 FALSE 11
#> # ℹ 140 more rows
#>
#>
#> $phenotype2
#> $phenotype2$sim_obs
#> # A tibble: 150 × 5
#> fid g_phenotype2 o_phenotype2 o_phenotype2_status o_phenotype2_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fid_1 -0.874 -0.723 FALSE 37
#> 2 fid_2 0.773 1.28 FALSE 20
#> 3 fid_3 1.38 0.282 FALSE 32
#> 4 fid_4 0.873 0.373 FALSE 21
#> 5 fid_5 -0.547 -0.691 FALSE 34
#> 6 fid_6 -0.0146 0.255 FALSE 31
#> 7 fid_7 -0.812 -0.282 FALSE 26
#> 8 fid_8 -0.893 -0.307 FALSE 34
#> 9 fid_9 0.449 0.222 FALSE 39
#> 10 fid_10 0.00115 -1.06 FALSE 11
#> # ℹ 140 more rows
#>
#>
#> $phenotype3
#> $phenotype3$sim_obs
#> # A tibble: 150 × 5
#> fid g_phenotype3 o_phenotype3 o_phenotype3_status o_phenotype3_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fid_1 -0.489 -0.175 FALSE 37
#> 2 fid_2 0.530 0.613 FALSE 20
#> 3 fid_3 0.606 0.0891 FALSE 32
#> 4 fid_4 0.538 0.608 FALSE 21
#> 5 fid_5 -0.187 -0.0331 FALSE 34
#> 6 fid_6 0.450 0.218 FALSE 31
#> 7 fid_7 -0.135 -0.428 FALSE 26
#> 8 fid_8 -0.124 0.757 FALSE 34
#> 9 fid_9 -1.04 -1.15 FALSE 39
#> 10 fid_10 -0.421 -0.461 FALSE 11
#> # ℹ 140 more rows
#>
#>
#> $thresholds
#> # A tibble: 150 × 9
#> fid indiv_ID role lower_phenotype1 upper_phenotype1 lower_phenotype2
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 fid_1 fid_1_1 o -Inf 2.55 -Inf
#> 2 fid_2 fid_2_1 o -Inf 3.21 -Inf
#> 3 fid_3 fid_3_1 o 1.45 1.45 -Inf
#> 4 fid_4 fid_4_1 o -Inf 3.17 -Inf
#> 5 fid_5 fid_5_1 o 1.39 1.39 -Inf
#> 6 fid_6 fid_6_1 o -Inf 2.79 -Inf
#> 7 fid_7 fid_7_1 o -Inf 2.99 -Inf
#> 8 fid_8 fid_8_1 o -Inf 2.68 -Inf
#> 9 fid_9 fid_9_1 o -Inf 2.47 -Inf
#> 10 fid_10 fid_10_1 o -Inf 3.52 -Inf
#> # ℹ 140 more rows
#> # ℹ 3 more variables: upper_phenotype2 <dbl>, lower_phenotype3 <dbl>,
#> # upper_phenotype3 <dbl>
#>