Title
Arguments
- n
TODO
- k
TODO
- ...
Arguments passed on to
model_mediator_informativeztheta_0TODO
ztheta_tTODO
ztheta_cTODO
ztheta_tcTODO
expected_degreeIf specified, the desired expected degree of the graph. Specifying
expected_degreesimply rescalesSto achieve this. Defaults toNULL. Do not specify bothexpected_degreeandexpected_densityat the same time.
Examples
set.seed(26)
mrdpg <- model_canonical(n = 100, k = 5)
graph <- sample_tidygraph(mrdpg)
graph
#> # A tbl_graph: 100 nodes and 7635 edges
#> #
#> # An undirected multigraph with 1 component
#> #
#> # Node Data: 100 × 7 (active)
#> name C1 C2 C3 C4 C5 y
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 2.75 0 0 0 0 -4.81
#> 2 2 2.64 0 0 0 0 -4.90
#> 3 3 2.60 0 0 0 0 -6.57
#> 4 4 2.59 0 0 0 0 -3.98
#> 5 5 2.47 0 0 0 0 -4.73
#> 6 6 2.34 0 0 0 0 -2.13
#> 7 7 2.12 0 0 0 0 -3.07
#> 8 8 2.10 0 0 0 0 -4.31
#> 9 9 1.94 0 0 0 0 -4.90
#> 10 10 1.68 0 0 0 0 -4.95
#> # ℹ 90 more rows
#> #
#> # Edge Data: 7,635 × 2
#> from to
#> <int> <int>
#> 1 4 16
#> 2 12 15
#> 3 7 13
#> # ℹ 7,632 more rows
m_fit <- nodelm(US(A, 5) ~ . - name - y - 1, graph = graph)
o_fit <- nodelm(y ~ . - name - 1 + US(A, 5), graph = graph)
m_fit
#>
#> Call:
#> stats::lm(formula = formula, data = data)
#>
#> Coefficients:
#> 1 2 3 4 5
#> C1 0.03059 -0.03385 0.04635 0.11249 -1.24261
#> C2 1.26626 0.08307 -0.04095 -0.01727 0.01139
#> C3 0.06434 -0.09365 1.23454 -0.12532 0.02745
#> C4 0.04564 -0.17560 0.18478 0.98795 0.08751
#> C5 0.09031 -1.15898 -0.08072 -0.02637 0.01566
#>
o_fit
#>
#> Call:
#> stats::lm(formula = formula, data = data)
#>
#> Coefficients:
#> C1 C2 C3 C4 C5 US(A, 5)1 US(A, 5)2
#> -1.5888 1.8275 0.4448 0.9288 1.5257 0.3891 1.1598
#> US(A, 5)3 US(A, 5)4 US(A, 5)5
#> 1.2010 -0.7468 0.2084
#>