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Simulation Settings for Feature Importance Methods
In xplainfi: Feature Importance Methods for Global Explanations
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 7,
fig.height = 5
)
library(xplainfi)
library(DiagrammeR)
library(mlr3learners)
set.seed(123)
Introduction
The xplainfi package provides several data generating processes (DGPs) designed to illustrate specific strengths and weaknesses of different feature importance methods. Each DGP focuses on one primary challenge to make the differences between methods clear.
This article provides a comprehensive overview of all simulation settings, including their mathematical formulations and causal structures visualized as directed acyclic graphs (DAGs).
Overview of Simulation Settings
dgp_overview <- data.frame(
DGP = c(
"sim_dgp_correlated",
"sim_dgp_mediated",
"sim_dgp_confounded",
"sim_dgp_interactions",
"sim_dgp_independent",
"sim_dgp_ewald"
),
Challenge = c(
"Spurious correlation",
"Mediation effects",
"Confounding",
"Interaction effects",
"Baseline (no challenges)",
"Mixed effects"
),
`PFI Behavior` = c(
"High for spurious x2",
"Shows total effects",
"Biased upward",
"Low (no main effects)",
"Accurate",
"Mixed"
),
`CFI Behavior` = c(
"Low for spurious x2",
"Shows direct effects",
"Less biased",
"High (captures interactions)",
"Accurate",
"Mixed"
),
check.names = FALSE
)
knitr::kable(dgp_overview, caption = "Overview of simulation settings and expected method behavior")
1. Correlated Features DGP
This DGP creates a highly correlated spurious predictor to illustrate the fundamental difference between marginal and conditional importance methods.
Mathematical Model
$$(X_1, X_2)^T \sim \text{MVN}(0, \Sigma)$$
where $\Sigma$ is a $2 \times 2$ covariance matrix with 1 on the diagonal and correlation $r$ (default 0.9) on the off-diagonal.
$$X_3 \sim N(0,1), \quad X_4 \sim N(0,1)$$
$$Y = 2 \cdot X_1 + X_3 + \varepsilon$$
where $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with four feature nodes (X1-X4) and outcome Y. Arrows show causal paths from X1 and X3 to Y, with an edge between X1 and X2 showing r approx. 0.9"
grViz(
"
digraph Correlated {
rankdir=LR;
graph [ranksep=1.5];
node [shape=circle, style=filled, fontsize=14, width=1.2];
X1 [fillcolor='lightcoral', label='X₁\n(β=2.0)'];
X2 [fillcolor='pink', label='X₂\n(β=0)'];
X3 [fillcolor='lightblue', label='X₃\n(β=1.0)'];
X4 [fillcolor='lightgray', label='X₄\n(β=0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
X1 -> X2 [color=red, style=bold, label='r≈0.9'];
X1 -> Y [label='2.0'];
X2 -> Y [style=dashed, color=gray, label='0'];
X3 -> Y [label='1.0'];
X4 -> Y [style=dashed, color=gray];
{rank=source; X1; X3; X4}
{rank=same; X2}
{rank=sink; Y}
}"
)
Usage Example
set.seed(123)
task <- sim_dgp_correlated(n = 500)
# Check correlation between X1 and X2
cor(task$data()[, c("x1", "x2")])
# True coefficients: x1=2.0, x2=0, x3=1.0, x4=0
# Note: x2 is highly correlated with x1 but has NO causal effect!
2. Mediated Effects DGP
This DGP demonstrates the difference between total and direct causal effects. Some features affect the outcome only through mediators.
Mathematical Model
$$\text{exposure} \sim N(0,1), \quad \text{direct} \sim N(0,1)$$
$$\text{mediator} = 0.8 \cdot \text{exposure} + 0.6 \cdot \text{direct} + \varepsilon_m$$
$$Y = 1.5 \cdot \text{mediator} + 0.5 \cdot \text{direct} + \varepsilon$$
where $\varepsilon_m \sim N(0, 0.3^2)$ and $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph showing mediation structure. Exposure connects to mediator, which connects to Y. Direct connects to both mediator and Y. Noise node has dashed arrow to Y."
grViz(
"
digraph Mediated {
rankdir=LR;
graph [ranksep=1.2];
node [shape=circle, style=filled, fontsize=14, width=1.2];
E [fillcolor='orange', label='Exposure\n(β=0)'];
D [fillcolor='lightblue', label='Direct\n(β=0.5)'];
M [fillcolor='yellow', label='Mediator\n(β=1.5)'];
N [fillcolor='lightgray', label='Noise\n(β=0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
E -> M [label='0.8', color=purple, penwidth=2];
D -> M [label='0.6', color=blue];
D -> Y [label='0.5', color=blue];
M -> Y [label='1.5', color=purple, penwidth=2];
N -> Y [style=dashed, color=gray];
{rank=source; E; D; N}
{rank=same; M}
{rank=sink; Y}
}"
)
Usage Example
set.seed(123)
task <- sim_dgp_mediated(n = 500)
# Calculate total effect of exposure
# Total effect = 0.8 * 1.5 = 1.2 (through mediator)
# Direct effect = 0 (no direct path to Y)
3. Confounding DGP
This DGP includes a confounder that affects both a feature and the outcome.
Mathematical Model
$$H \sim N(0,1) \quad \text{(confounder)}$$
$$X_1 = H + \varepsilon_1$$
$$\text{proxy} = H + \varepsilon_p, \quad \text{independent} \sim N(0,1)$$
$$Y = H + X_1 + \text{independent} + \varepsilon$$
where all $\varepsilon \sim N(0, 0.5^2)$ independently.
Causal Structure
#| fig.alt: "Directed acyclic graph with hidden confounder H (dashed circle) connecting to X1, proxy, and Y. X1 and independent also have arrows to Y."
grViz(
"
digraph Confounded {
rankdir=LR;
graph [ranksep=1.2, nodesep=0.8];
node [shape=circle, style=filled, fontsize=14, width=1.2];
H [fillcolor='red', label='H\n(Confounder)', style='filled,dashed'];
X1 [fillcolor='lightcoral', label='X₁\n(β=1.0)'];
P [fillcolor='pink', label='Proxy\n(β=0)'];
I [fillcolor='lightblue', label='Independent\n(β=1.0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
H -> X1 [color=red, label='1.0'];
H -> P [color=red, style=dashed, label='1.0'];
H -> Y [color=red, label='1.0', penwidth=2];
X1 -> Y [label='1.0'];
I -> Y [label='1.0'];
{rank=source; H}
{rank=same; X1; P; I}
{rank=sink; Y}
}"
)
Usage Example
set.seed(123)
# Hidden confounder scenario (default)
task_hidden <- sim_dgp_confounded(n = 500, hidden = TRUE)
task_hidden$feature_names # proxy available but not confounder
# Observable confounder scenario
task_observed <- sim_dgp_confounded(n = 500, hidden = FALSE)
task_observed$feature_names # both confounder and proxy available
4. Interaction Effects DGP
This DGP demonstrates a pure interaction effect where features have no main effects.
Mathematical Model
$$Y = 2 \cdot X_1 \cdot X_2 + X_3 + \varepsilon$$
where $X_j \sim N(0,1)$ independently and $\varepsilon \sim N(0, 0.5^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with X1 and X2 connecting to a diamond-shaped interaction node, which connects to Y. X3 connects directly to Y. N1 and N2 have dashed arrows to Y."
grViz(
"
digraph Interaction {
rankdir=LR;
graph [ranksep=1.2];
node [shape=circle, style=filled, fontsize=14, width=1.2];
X1 [fillcolor='orange', label='X₁\n(β=0)'];
X2 [fillcolor='orange', label='X₂\n(β=0)'];
X3 [fillcolor='lightblue', label='X₃\n(β=1.0)'];
N1 [fillcolor='lightgray', label='N₁\n(β=0)'];
N2 [fillcolor='lightgray', label='N₂\n(β=0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
INT [fillcolor='red', shape=diamond, label='X₁×X₂\n(β=2.0)', width=1.5];
X1 -> INT [color=red, penwidth=2];
X2 -> INT [color=red, penwidth=2];
INT -> Y [color=red, label='2.0', penwidth=2];
X3 -> Y [label='1.0'];
N1 -> Y [style=dashed, color=gray];
N2 -> Y [style=dashed, color=gray];
{rank=source; X1; X2; X3; N1; N2}
{rank=same; INT}
{rank=sink; Y}
}"
)
Usage Example
set.seed(123)
task <- sim_dgp_interactions(n = 500)
# Note: X1 and X2 have NO main effects
# Their importance comes ONLY through their interaction
5. Independent Features DGP (Baseline)
This is a baseline scenario where all features are independent and their effects are additive. All importance methods should give similar results.
Mathematical Model
$$Y = 2.0 \cdot X_1 + 1.0 \cdot X_2 + 0.5 \cdot X_3 + \varepsilon$$
where $X_j \sim N(0,1)$ independently and $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with five feature nodes in a row, each with arrows pointing to outcome Y. X1-X3 have solid arrows with effect sizes labeled, N1-N2 have dashed arrows."
grViz(
"
digraph Independent {
rankdir=LR;
graph [ranksep=1.5];
node [shape=circle, style=filled, fontsize=14, width=1.2];
X1 [fillcolor='lightblue', label='X₁\n(β=2.0)'];
X2 [fillcolor='lightblue', label='X₂\n(β=1.0)'];
X3 [fillcolor='lightblue', label='X₃\n(β=0.5)'];
N1 [fillcolor='lightgray', label='N₁\n(β=0)'];
N2 [fillcolor='lightgray', label='N₂\n(β=0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
X1 -> Y [label='2.0', penwidth=3];
X2 -> Y [label='1.0', penwidth=2];
X3 -> Y [label='0.5'];
N1 -> Y [style=dashed, color=gray];
N2 -> Y [style=dashed, color=gray];
{rank=source; X1; X2; X3; N1; N2}
{rank=sink; Y}
}"
)
Usage Example
set.seed(123)
task <- sim_dgp_independent(n = 500)
# All methods should rank features consistently:
# important1 > important2 > important3 > unimportant1,2 (approx. 0)
Expected Behavior
- All methods: Should rank features consistently by their true effect sizes
- Ground truth: important1 (2.0) > important2 (1.0) > important3 (0.5) > unimportant1,2 (0)
6. Ewald et al. (2024) DGP
Reproduces the data generating process from Ewald et al. (2024) for benchmarking feature importance methods. Includes correlated features and interaction effects.
Mathematical Model
$$X_1, X_3, X_5 \sim \text{Uniform}(0,1)$$
$$X_2 = X_1 + \varepsilon_2, \quad \varepsilon_2 \sim N(0, 0.001)$$
$$X_4 = X_3 + \varepsilon_4, \quad \varepsilon_4 \sim N(0, 0.1)$$
$$Y = X_4 + X_5 + X_4 \cdot X_5 + \varepsilon, \quad \varepsilon \sim N(0, 0.1)$$
Causal Structure
#| fig.alt: "Directed acyclic graph with X1-X2 correlation, X3-X4 correlation. X4 and X5 connect to a diamond interaction node and directly to Y."
grViz(
"
digraph Ewald {
rankdir=LR;
graph [ranksep=1.2];
node [shape=circle, style=filled, fontsize=14, width=1.2];
X1 [fillcolor='lightgray', label='X₁\n(β=0)'];
X2 [fillcolor='lightgray', label='X₂\n(β=0)'];
X3 [fillcolor='lightgray', label='X₃\n(β=0)'];
X4 [fillcolor='lightblue', label='X₄\n(β=1.0)'];
X5 [fillcolor='lightblue', label='X₅\n(β=1.0)'];
Y [fillcolor='greenyellow', label='Y', width=1.5];
INT [fillcolor='red', shape=diamond, label='X₄×X₅\n(β=1.0)', width=1.5];
X1 -> X2 [color=gray, label='≈1.0'];
X3 -> X4 [color=gray, label='≈1.0'];
X4 -> Y [label='1.0'];
X5 -> Y [label='1.0'];
X4 -> INT [color=red];
X5 -> INT [color=red];
INT -> Y [color=red, label='1.0'];
{rank=source; X1; X3; X5}
{rank=same; X2; X4}
{rank=same; INT}
{rank=sink; Y}
}"
)
Usage Example
sim_dgp_ewald(n = 500)
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xplainfi documentation built on Feb. 27, 2026, 1:08 a.m.
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knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 7, fig.height = 5 )
library(xplainfi) library(DiagrammeR) library(mlr3learners) set.seed(123)
Introduction
The xplainfi package provides several data generating processes (DGPs) designed to illustrate specific strengths and weaknesses of different feature importance methods. Each DGP focuses on one primary challenge to make the differences between methods clear.
This article provides a comprehensive overview of all simulation settings, including their mathematical formulations and causal structures visualized as directed acyclic graphs (DAGs).
Overview of Simulation Settings
dgp_overview <- data.frame( DGP = c( "sim_dgp_correlated", "sim_dgp_mediated", "sim_dgp_confounded", "sim_dgp_interactions", "sim_dgp_independent", "sim_dgp_ewald" ), Challenge = c( "Spurious correlation", "Mediation effects", "Confounding", "Interaction effects", "Baseline (no challenges)", "Mixed effects" ), `PFI Behavior` = c( "High for spurious x2", "Shows total effects", "Biased upward", "Low (no main effects)", "Accurate", "Mixed" ), `CFI Behavior` = c( "Low for spurious x2", "Shows direct effects", "Less biased", "High (captures interactions)", "Accurate", "Mixed" ), check.names = FALSE ) knitr::kable(dgp_overview, caption = "Overview of simulation settings and expected method behavior")
1. Correlated Features DGP
This DGP creates a highly correlated spurious predictor to illustrate the fundamental difference between marginal and conditional importance methods.
Mathematical Model
$$(X_1, X_2)^T \sim \text{MVN}(0, \Sigma)$$
where $\Sigma$ is a $2 \times 2$ covariance matrix with 1 on the diagonal and correlation $r$ (default 0.9) on the off-diagonal.
$$X_3 \sim N(0,1), \quad X_4 \sim N(0,1)$$ $$Y = 2 \cdot X_1 + X_3 + \varepsilon$$
where $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with four feature nodes (X1-X4) and outcome Y. Arrows show causal paths from X1 and X3 to Y, with an edge between X1 and X2 showing r approx. 0.9" grViz( " digraph Correlated { rankdir=LR; graph [ranksep=1.5]; node [shape=circle, style=filled, fontsize=14, width=1.2]; X1 [fillcolor='lightcoral', label='X₁\n(β=2.0)']; X2 [fillcolor='pink', label='X₂\n(β=0)']; X3 [fillcolor='lightblue', label='X₃\n(β=1.0)']; X4 [fillcolor='lightgray', label='X₄\n(β=0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; X1 -> X2 [color=red, style=bold, label='r≈0.9']; X1 -> Y [label='2.0']; X2 -> Y [style=dashed, color=gray, label='0']; X3 -> Y [label='1.0']; X4 -> Y [style=dashed, color=gray]; {rank=source; X1; X3; X4} {rank=same; X2} {rank=sink; Y} }" )
Usage Example
set.seed(123) task <- sim_dgp_correlated(n = 500) # Check correlation between X1 and X2 cor(task$data()[, c("x1", "x2")]) # True coefficients: x1=2.0, x2=0, x3=1.0, x4=0 # Note: x2 is highly correlated with x1 but has NO causal effect!
2. Mediated Effects DGP
This DGP demonstrates the difference between total and direct causal effects. Some features affect the outcome only through mediators.
Mathematical Model
$$\text{exposure} \sim N(0,1), \quad \text{direct} \sim N(0,1)$$ $$\text{mediator} = 0.8 \cdot \text{exposure} + 0.6 \cdot \text{direct} + \varepsilon_m$$ $$Y = 1.5 \cdot \text{mediator} + 0.5 \cdot \text{direct} + \varepsilon$$
where $\varepsilon_m \sim N(0, 0.3^2)$ and $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph showing mediation structure. Exposure connects to mediator, which connects to Y. Direct connects to both mediator and Y. Noise node has dashed arrow to Y." grViz( " digraph Mediated { rankdir=LR; graph [ranksep=1.2]; node [shape=circle, style=filled, fontsize=14, width=1.2]; E [fillcolor='orange', label='Exposure\n(β=0)']; D [fillcolor='lightblue', label='Direct\n(β=0.5)']; M [fillcolor='yellow', label='Mediator\n(β=1.5)']; N [fillcolor='lightgray', label='Noise\n(β=0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; E -> M [label='0.8', color=purple, penwidth=2]; D -> M [label='0.6', color=blue]; D -> Y [label='0.5', color=blue]; M -> Y [label='1.5', color=purple, penwidth=2]; N -> Y [style=dashed, color=gray]; {rank=source; E; D; N} {rank=same; M} {rank=sink; Y} }" )
Usage Example
set.seed(123) task <- sim_dgp_mediated(n = 500) # Calculate total effect of exposure # Total effect = 0.8 * 1.5 = 1.2 (through mediator) # Direct effect = 0 (no direct path to Y)
3. Confounding DGP
This DGP includes a confounder that affects both a feature and the outcome.
Mathematical Model
$$H \sim N(0,1) \quad \text{(confounder)}$$ $$X_1 = H + \varepsilon_1$$ $$\text{proxy} = H + \varepsilon_p, \quad \text{independent} \sim N(0,1)$$ $$Y = H + X_1 + \text{independent} + \varepsilon$$
where all $\varepsilon \sim N(0, 0.5^2)$ independently.
Causal Structure
#| fig.alt: "Directed acyclic graph with hidden confounder H (dashed circle) connecting to X1, proxy, and Y. X1 and independent also have arrows to Y." grViz( " digraph Confounded { rankdir=LR; graph [ranksep=1.2, nodesep=0.8]; node [shape=circle, style=filled, fontsize=14, width=1.2]; H [fillcolor='red', label='H\n(Confounder)', style='filled,dashed']; X1 [fillcolor='lightcoral', label='X₁\n(β=1.0)']; P [fillcolor='pink', label='Proxy\n(β=0)']; I [fillcolor='lightblue', label='Independent\n(β=1.0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; H -> X1 [color=red, label='1.0']; H -> P [color=red, style=dashed, label='1.0']; H -> Y [color=red, label='1.0', penwidth=2]; X1 -> Y [label='1.0']; I -> Y [label='1.0']; {rank=source; H} {rank=same; X1; P; I} {rank=sink; Y} }" )
Usage Example
set.seed(123) # Hidden confounder scenario (default) task_hidden <- sim_dgp_confounded(n = 500, hidden = TRUE) task_hidden$feature_names # proxy available but not confounder # Observable confounder scenario task_observed <- sim_dgp_confounded(n = 500, hidden = FALSE) task_observed$feature_names # both confounder and proxy available
4. Interaction Effects DGP
This DGP demonstrates a pure interaction effect where features have no main effects.
Mathematical Model
$$Y = 2 \cdot X_1 \cdot X_2 + X_3 + \varepsilon$$
where $X_j \sim N(0,1)$ independently and $\varepsilon \sim N(0, 0.5^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with X1 and X2 connecting to a diamond-shaped interaction node, which connects to Y. X3 connects directly to Y. N1 and N2 have dashed arrows to Y." grViz( " digraph Interaction { rankdir=LR; graph [ranksep=1.2]; node [shape=circle, style=filled, fontsize=14, width=1.2]; X1 [fillcolor='orange', label='X₁\n(β=0)']; X2 [fillcolor='orange', label='X₂\n(β=0)']; X3 [fillcolor='lightblue', label='X₃\n(β=1.0)']; N1 [fillcolor='lightgray', label='N₁\n(β=0)']; N2 [fillcolor='lightgray', label='N₂\n(β=0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; INT [fillcolor='red', shape=diamond, label='X₁×X₂\n(β=2.0)', width=1.5]; X1 -> INT [color=red, penwidth=2]; X2 -> INT [color=red, penwidth=2]; INT -> Y [color=red, label='2.0', penwidth=2]; X3 -> Y [label='1.0']; N1 -> Y [style=dashed, color=gray]; N2 -> Y [style=dashed, color=gray]; {rank=source; X1; X2; X3; N1; N2} {rank=same; INT} {rank=sink; Y} }" )
Usage Example
set.seed(123) task <- sim_dgp_interactions(n = 500) # Note: X1 and X2 have NO main effects # Their importance comes ONLY through their interaction
5. Independent Features DGP (Baseline)
This is a baseline scenario where all features are independent and their effects are additive. All importance methods should give similar results.
Mathematical Model
$$Y = 2.0 \cdot X_1 + 1.0 \cdot X_2 + 0.5 \cdot X_3 + \varepsilon$$
where $X_j \sim N(0,1)$ independently and $\varepsilon \sim N(0, 0.2^2)$.
Causal Structure
#| fig.alt: "Directed acyclic graph with five feature nodes in a row, each with arrows pointing to outcome Y. X1-X3 have solid arrows with effect sizes labeled, N1-N2 have dashed arrows." grViz( " digraph Independent { rankdir=LR; graph [ranksep=1.5]; node [shape=circle, style=filled, fontsize=14, width=1.2]; X1 [fillcolor='lightblue', label='X₁\n(β=2.0)']; X2 [fillcolor='lightblue', label='X₂\n(β=1.0)']; X3 [fillcolor='lightblue', label='X₃\n(β=0.5)']; N1 [fillcolor='lightgray', label='N₁\n(β=0)']; N2 [fillcolor='lightgray', label='N₂\n(β=0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; X1 -> Y [label='2.0', penwidth=3]; X2 -> Y [label='1.0', penwidth=2]; X3 -> Y [label='0.5']; N1 -> Y [style=dashed, color=gray]; N2 -> Y [style=dashed, color=gray]; {rank=source; X1; X2; X3; N1; N2} {rank=sink; Y} }" )
Usage Example
set.seed(123) task <- sim_dgp_independent(n = 500) # All methods should rank features consistently: # important1 > important2 > important3 > unimportant1,2 (approx. 0)
Expected Behavior
- All methods: Should rank features consistently by their true effect sizes
- Ground truth: important1 (2.0) > important2 (1.0) > important3 (0.5) > unimportant1,2 (0)
6. Ewald et al. (2024) DGP
Reproduces the data generating process from Ewald et al. (2024) for benchmarking feature importance methods. Includes correlated features and interaction effects.
Mathematical Model
$$X_1, X_3, X_5 \sim \text{Uniform}(0,1)$$ $$X_2 = X_1 + \varepsilon_2, \quad \varepsilon_2 \sim N(0, 0.001)$$ $$X_4 = X_3 + \varepsilon_4, \quad \varepsilon_4 \sim N(0, 0.1)$$ $$Y = X_4 + X_5 + X_4 \cdot X_5 + \varepsilon, \quad \varepsilon \sim N(0, 0.1)$$
Causal Structure
#| fig.alt: "Directed acyclic graph with X1-X2 correlation, X3-X4 correlation. X4 and X5 connect to a diamond interaction node and directly to Y." grViz( " digraph Ewald { rankdir=LR; graph [ranksep=1.2]; node [shape=circle, style=filled, fontsize=14, width=1.2]; X1 [fillcolor='lightgray', label='X₁\n(β=0)']; X2 [fillcolor='lightgray', label='X₂\n(β=0)']; X3 [fillcolor='lightgray', label='X₃\n(β=0)']; X4 [fillcolor='lightblue', label='X₄\n(β=1.0)']; X5 [fillcolor='lightblue', label='X₅\n(β=1.0)']; Y [fillcolor='greenyellow', label='Y', width=1.5]; INT [fillcolor='red', shape=diamond, label='X₄×X₅\n(β=1.0)', width=1.5]; X1 -> X2 [color=gray, label='≈1.0']; X3 -> X4 [color=gray, label='≈1.0']; X4 -> Y [label='1.0']; X5 -> Y [label='1.0']; X4 -> INT [color=red]; X5 -> INT [color=red]; INT -> Y [color=red, label='1.0']; {rank=source; X1; X3; X5} {rank=same; X2; X4} {rank=same; INT} {rank=sink; Y} }" )
Usage Example
sim_dgp_ewald(n = 500)
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