Simulate multi-platform data from multiple Gaussian graphical models

Generates S platforms of data, each with K groups sharing structure across groups (controlled by perturb_prob) and across platforms (controlled by platform_perturb_prob). Follows Shaddox et al. (2020) simulation design. Platform 1 uses the base graph; platforms 2..S perturb it.

Usage

simulate_multiggm_platform(
  K = 2,
  S = 2,
  p_vec = c(20, 15),
  n = 100,
  graph_type = c("band", "random", "hub"),
  perturb_prob = 0.1,
  platform_perturb_prob = 0.2,
  signal = c(0.3, 0.6),
  seed = NULL
)

Arguments

K

Integer; number of sample groups. Default 2.

S

Integer; number of platforms (data types). Default 2.

p_vec

Integer vector of length S; number of variables per platform. Default c(20, 15).

n

Integer (scalar or length-K vector); sample size per group (same across platforms). Default 100.

graph_type

Character; type of base graph. See simulate_multiggm.

perturb_prob

Numeric; probability of edge flip between groups within a platform. Default 0.1.

platform_perturb_prob

Numeric; probability of edge flip between platforms. Default 0.2.

signal

Numeric vector of length 2; magnitude range for off-diagonal precision entries. Default c(0.3, 0.6).

seed

Optional integer random seed.

Value

A list with components:

• platform_data: List of S elements, each suitable as input to multiggm_mcmc(method = "ssvs_platform", platform_data = ...). Each element is a list with data_list, S_list, n_vec, Omega_list, and adj_list.

• K, S, p_vec: Dimensions.

See also

simulate_multiggm(), multiggm_mcmc()

Examples

sim <- simulate_multiggm_platform(K = 2, S = 2, p_vec = c(10, 8),
                                   n = 80, seed = 42)
str(sim, max.level = 2)
#> List of 4
#>  $ platform_data:List of 2
#>   ..$ :List of 5
#>   ..$ :List of 5
#>  $ K            : num 2
#>  $ S            : num 2
#>  $ p_vec        : int [1:2] 10 8