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Generate sample data with a latent confounder (for BottomUpParceLiNGAM)

Source: R/generate_parce_sample.r
generate_parce_sample.Rd

Generates the 7-variable model used in the ParceLiNGAM tutorial, where x6 is an unobserved (latent) common cause of x2 and x3. Only x0-x5 are returned as observed data; x6 is not included.

Usage

generate_parce_sample(n = 1000L, seed = NULL)

Arguments

n

number of samples (default: 1000)

seed

random seed (default: NULL, i.e. do not reset the RNG state)

Value

list with three elements:

  • data: data.frame of the 6 observed variables (x0-x5).

  • adjacency_matrix: the true 6x6 adjacency matrix among the observed variables, following the m[to, from] convention. The x2-x3 entries (which share the latent confounder x6 and have no direct edge between them) are NA, matching the convention used by lingam_parce().

  • confounded_pair: 1-based column positions of x2 and x3 (the pair sharing the latent confounder).

Details

The data-generating process (all error terms are Uniform(0, 1)):


x6 (latent) ~ Uniform(0, 1)
x3 = 2.0 * x6 + e
x2 = 2.0 * x6 + e
x0 = 0.5 * x3 + e
x1 = 0.5 * x0 + 0.5 * x2 + e
x5 = 0.5 * x0 + e
x4 = 0.5 * x0 - 0.5 * x2 + e

Examples

confounded <- generate_parce_sample(n = 500, seed = 42)
head(confounded$data)
#>          x0       x1       x2       x3          x4        x5
#> 1 1.0369696 2.847403 2.677905 1.966117 -0.54667538 0.5788658
#> 2 1.9807394 2.397312 1.936897 2.051287  0.96611782 1.9233741
#> 3 0.5715207 1.681726 1.392124 1.091840  0.03568159 0.6347020
#> 4 2.1567712 3.067590 2.200256 2.472016  0.52004497 1.4901835
#> 5 1.0660913 2.258461 1.782511 1.398853 -0.19645547 1.4941921
#> 6 1.6641994 2.096731 1.060419 1.931614  0.99494363 1.3631726
confounded$adjacency_matrix
#>     x0 x1   x2  x3 x4 x5
#> x0 0.0  0  0.0 0.5  0  0
#> x1 0.5  0  0.5 0.0  0  0
#> x2 0.0  0  0.0  NA  0  0
#> x3 0.0  0   NA 0.0  0  0
#> x4 0.5  0 -0.5 0.0  0  0
#> x5 0.5  0  0.0 0.0  0  0
confounded$confounded_pair
#> [1] 3 4