Simulate under the liability threshold model (single phenotype).
Source:R/Simulate_under_LTM.R
simulate_under_LTM_single.Rdsimulate_under_LTM_single simulates families and thresholds under
the liability threshold model for a given family structure and a single
phenotype. Please note that it is not possible to simulate different
family structures.
Usage
simulate_under_LTM_single(
fam_vec = c("m", "f", "s1", "mgm", "mgf", "pgm", "pgf"),
n_fam = NULL,
add_ind = TRUE,
h2 = 0.5,
n_sim = 1000,
pop_prev = 0.1
)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.- h2
A number representing the liability-scale heritability for a single phenotype. Must be non-negative. Note that under the liability threshold model, the heritability must also be at most 1. Defaults to 0.5.
- n_sim
A positive number representing the number of simulations. Defaults to 1000.
- pop_prev
A positive number representing the population prevalence, i.e. the overall prevalence in the population. Must be smaller than 1. Defaults to 0.1.
Value
If either fam_vec or n_fam is used as the argument,
if it is of the required format, if the liability-scale heritability h2
is a number satisfying \(0 \leq h^2\), n_sim is a strictly positive number,
and pop_prev is a positive number that is at most one,
then the output will be a list holding two tibbles.
The first tibble, sim_obs, 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.
The second tibble, 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.
Note that this tibble has the format required in estimate_liability.
In addition, 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_single()
#> $sim_obs
#> # A tibble: 1,000 × 26
#> fam_ID g o m f s1 mgm mgf pgm pgf
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 fam_ID_1 0.540 1.05 0.0659 -0.226 2.14 -1.08 -1.11 -0.354 0.854
#> 2 fam_ID_2 -0.265 1.64 0.844 0.293 -0.777 -0.179 -0.372 1.99 -1.02
#> 3 fam_ID_3 -1.10 -2.00 -0.205 -0.820 1.02 -0.944 1.74 0.0201 -1.19
#> 4 fam_ID_4 -0.344 0.342 1.46 0.313 0.476 -0.0829 -0.107 -0.734 1.44
#> 5 fam_ID_5 0.152 1.78 -1.21 -0.510 0.949 0.204 0.130 -0.141 -0.187
#> 6 fam_ID_6 -0.132 0.659 -0.282 0.160 -0.772 -1.52 -1.43 -1.56 -0.911
#> 7 fam_ID_7 1.43 1.50 -0.489 0.287 1.45 -0.844 0.255 1.53 -0.283
#> 8 fam_ID_8 -0.107 0.597 -0.277 0.192 -0.997 -1.22 1.29 0.989 -0.477
#> 9 fam_ID_9 0.00789 -0.364 0.482 1.14 1.73 0.158 -1.03 -0.618 -0.0198
#> 10 fam_ID_… 0.289 0.360 -0.154 0.359 1.42 0.979 0.106 1.57 0.146
#> # ℹ 990 more rows
#> # ℹ 16 more variables: o_status <lgl>, m_status <lgl>, f_status <lgl>,
#> # s1_status <lgl>, mgm_status <lgl>, mgf_status <lgl>, pgm_status <lgl>,
#> # pgf_status <lgl>, o_aoo <dbl>, m_aoo <dbl>, f_aoo <dbl>, s1_aoo <dbl>,
#> # mgm_aoo <dbl>, mgf_aoo <dbl>, pgm_aoo <dbl>, pgf_aoo <dbl>
#>
#> $thresholds
#> # A tibble: 8,000 × 5
#> fam_ID indiv_ID role lower upper
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o -Inf 2.47
#> 2 fam_ID_2 fam_ID_2_1 o 1.64 1.64
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.91
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.24
#> 5 fam_ID_5 fam_ID_5_1 o 1.78 1.78
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 3.21
#> 7 fam_ID_7 fam_ID_7_1 o 1.51 1.51
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 3.48
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 2.83
#> 10 fam_ID_10 fam_ID_10_1 o -Inf 2.72
#> # ℹ 7,990 more rows
#>
simulate_under_LTM_single(fam_vec = NULL, n_fam = stats::setNames(c(1,1,1,2),
c("m","mgm","mgf","mhs")))
#> $sim_obs
#> # A tibble: 1,000 × 20
#> fam_ID g o m mgm mgf mhs1 mhs2 o_status m_status
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl> <lgl>
#> 1 fam_I… -0.0389 1.00 -0.173 0.122 1.26 -2.11 -0.657 FALSE FALSE
#> 2 fam_I… 0.172 -0.145 -0.274 0.642 -0.477 0.680 -0.115 FALSE FALSE
#> 3 fam_I… -0.602 -0.985 -2.19 -0.558 1.18 -0.698 -1.54 FALSE FALSE
#> 4 fam_I… 0.512 0.971 -0.159 1.08 -1.25 -1.18 -0.685 FALSE FALSE
#> 5 fam_I… 0.818 0.879 0.173 -0.874 0.564 -0.424 0.0713 FALSE FALSE
#> 6 fam_I… -0.190 1.26 -0.511 -0.366 0.162 -0.335 -0.0346 FALSE FALSE
#> 7 fam_I… 0.0386 0.162 2.22 0.251 1.88 0.495 0.134 FALSE TRUE
#> 8 fam_I… 0.0758 -0.0225 0.205 -0.129 0.565 -0.671 2.25 FALSE FALSE
#> 9 fam_I… 0.170 -0.896 -0.253 2.70 0.778 0.209 -0.183 FALSE FALSE
#> 10 fam_I… -0.507 -0.800 0.338 -0.527 0.167 -0.0751 0.660 FALSE FALSE
#> # ℹ 990 more rows
#> # ℹ 10 more variables: mgm_status <lgl>, mgf_status <lgl>, mhs1_status <lgl>,
#> # mhs2_status <lgl>, o_aoo <dbl>, m_aoo <dbl>, mgm_aoo <dbl>, mgf_aoo <dbl>,
#> # mhs1_aoo <dbl>, mhs2_aoo <dbl>
#>
#> $thresholds
#> # A tibble: 6,000 × 5
#> fam_ID indiv_ID role lower upper
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o -Inf 2.83
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 3.35
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 3.35
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.48
#> 5 fam_ID_5 fam_ID_5_1 o -Inf 2.51
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 2.72
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 2.79
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 2.63
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 3.55
#> 10 fam_ID_10 fam_ID_10_1 o -Inf 2.55
#> # ℹ 5,990 more rows
#>
simulate_under_LTM_single(fam_vec = c("m","f","s1"), n_fam = NULL, add_ind = FALSE,
h2 = 0.5, n_sim = 500, pop_prev = .05)
#> $sim_obs
#> # A tibble: 500 × 10
#> fam_ID m f s1 m_status f_status s1_status m_aoo f_aoo s1_aoo
#> <chr> <dbl> <dbl> <dbl> <lgl> <lgl> <lgl> <dbl> <dbl> <dbl>
#> 1 fam_ID… -0.558 -1.37 -0.654 FALSE FALSE FALSE 45 48 19
#> 2 fam_ID… 0.0812 0.259 0.274 FALSE FALSE FALSE 35 40 16
#> 3 fam_ID… -1.55 -0.416 -1.83 FALSE FALSE FALSE 37 34 10
#> 4 fam_ID… -0.706 1.83 -0.0682 FALSE TRUE FALSE 61 66 33
#> 5 fam_ID… -0.234 -1.20 0.413 FALSE FALSE FALSE 54 59 30
#> 6 fam_ID… 1.68 1.30 2.08 TRUE FALSE TRUE 80 51 56
#> 7 fam_ID… -1.33 0.592 -1.59 FALSE FALSE FALSE 54 64 36
#> 8 fam_ID… 1.26 1.65 0.329 FALSE TRUE FALSE 67 92 39
#> 9 fam_ID… 0.341 -0.831 0.0608 FALSE FALSE FALSE 51 40 21
#> 10 fam_ID… -1.06 -0.768 -1.92 FALSE FALSE FALSE 57 48 27
#> # ℹ 490 more rows
#>
#> $thresholds
#> # A tibble: 1,500 × 5
#> fam_ID indiv_ID role lower upper
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 m -Inf 2.48
#> 2 fam_ID_2 fam_ID_2_1 m -Inf 2.86
#> 3 fam_ID_3 fam_ID_3_1 m -Inf 2.79
#> 4 fam_ID_4 fam_ID_4_1 m -Inf 1.93
#> 5 fam_ID_5 fam_ID_5_1 m -Inf 2.14
#> 6 fam_ID_6 fam_ID_6_1 m 1.68 1.68
#> 7 fam_ID_7 fam_ID_7_1 m -Inf 2.14
#> 8 fam_ID_8 fam_ID_8_1 m -Inf 1.81
#> 9 fam_ID_9 fam_ID_9_1 m -Inf 2.25
#> 10 fam_ID_10 fam_ID_10_1 m -Inf 2.05
#> # ℹ 1,490 more rows
#>
simulate_under_LTM_single(fam_vec = c(), n_fam = NULL, add_ind = TRUE, h2 = 0.5,
n_sim = 200, pop_prev = 0.05)
#> Warning: Neither fam_vec nor n_fam is specified...
#> $sim_obs
#> # A tibble: 200 × 5
#> fam_ID g o o_status o_aoo
#> <chr> <dbl> <dbl> <lgl> <dbl>
#> 1 fam_ID_1 -1.27 -0.439 FALSE 25
#> 2 fam_ID_2 -0.553 -0.0114 FALSE 39
#> 3 fam_ID_3 -0.687 -0.850 FALSE 40
#> 4 fam_ID_4 0.535 -1.09 FALSE 20
#> 5 fam_ID_5 1.70 3.09 TRUE 29
#> 6 fam_ID_6 -0.783 -1.56 FALSE 10
#> 7 fam_ID_7 -0.243 0.0254 FALSE 30
#> 8 fam_ID_8 0.609 0.733 FALSE 13
#> 9 fam_ID_9 -0.178 -0.0371 FALSE 16
#> 10 fam_ID_10 -0.953 -1.09 FALSE 37
#> # ℹ 190 more rows
#>
#> $thresholds
#> # A tibble: 200 × 5
#> fam_ID indiv_ID role lower upper
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 fam_ID_1 fam_ID_1_1 o -Inf 3.23
#> 2 fam_ID_2 fam_ID_2_1 o -Inf 2.71
#> 3 fam_ID_3 fam_ID_3_1 o -Inf 2.67
#> 4 fam_ID_4 fam_ID_4_1 o -Inf 3.40
#> 5 fam_ID_5 fam_ID_5_1 o 3.09 3.09
#> 6 fam_ID_6 fam_ID_6_1 o -Inf 3.73
#> 7 fam_ID_7 fam_ID_7_1 o -Inf 3.05
#> 8 fam_ID_8 fam_ID_8_1 o -Inf 3.63
#> 9 fam_ID_9 fam_ID_9_1 o -Inf 3.54
#> 10 fam_ID_10 fam_ID_10_1 o -Inf 2.79
#> # ℹ 190 more rows
#>