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Generate a Graphviz DOT Diagram for an NMF-SEM Model

Source: R/nmf.sem.R
nmf.sem.DOT.Rd

Creates a Graphviz DOT script that visualizes the structural network estimated by nmf.sem. The resulting diagram displays:

  • endogenous observed variables (\(Y_1\)),

  • exogenous observed variables (\(Y_2\)),

  • latent factors (\(F_1\), ..., \(F_Q\)),

together with the non-negative path coefficients whose magnitudes exceed a user-specified threshold.

Directed edges represent estimated relationships:

  • \(Y_2 \rightarrow F_q\): entries of C2 (exogenous loadings),

  • \(F_q \rightarrow Y_1\): rows of X (factor-to-endogenous mappings),

  • \(Y_1 \rightarrow F_q\): entries of C1 (feedback paths).

Edge widths are scaled by coefficient magnitude, and nodes are placed in optional visual clusters. Only variables participating in edges above the threshold are displayed, while latent factors are always shown.

Usage

nmf.sem.DOT(
  result,
  weight_scale = 5,
  weight_scale_y2f = weight_scale,
  weight_scale_fy1 = weight_scale,
  weight_scale_feedback = weight_scale,
  threshold = 0.01,
  rankdir = "LR",
  fill = TRUE,
  cluster.box = c("normal", "faint", "invisible", "none"),
  cluster.labels = NULL,
  hide.isolated = TRUE,
  sig.level = 0.1
)

Arguments

result

A list returned by nmf.sem, containing matrices X, C1, and C2.

weight_scale

Base scaling factor for edge widths.

weight_scale_y2f

Optional override for scaling edges \(Y_2 \rightarrow F_q\). Defaults to weight_scale.

weight_scale_fy1

Optional override for scaling edges \(F_q \rightarrow Y_1\). Defaults to weight_scale.

weight_scale_feedback

Optional override for scaling feedback edges \(Y_1 \rightarrow F_q\). Defaults to weight_scale.

threshold

Minimum coefficient value needed for an edge to be drawn.

rankdir

Graphviz rank direction (e.g., "LR", "TB").

fill

Logical; whether to use filled node shapes.

cluster.box

Character string controlling the visibility and style of cluster frames around Y2, factors, and Y1 blocks. One of "normal", "faint", "invisible", "none".

cluster.labels

Optional character vector of length 3 giving custom labels for the Y2, factor, and Y1 clusters.

hide.isolated

Logical. If TRUE (default), Y1 and Y2 nodes that have no edges at or above threshold are excluded from the graph.

sig.level

Significance level for filtering C2 edges when inference results are present. If result contains a coefficients data frame (from nmf.sem.inference), only edges with p_value < sig.level are drawn, with significance stars appended. Set to NULL to disable filtering. Default is 0.1.

Value

A character string representing a valid Graphviz DOT script.

See also

nmf.sem, nmf.sem.inference, plot.nmfkc.DOT

Examples

Y <- t(iris[, -5])
Y1 <- Y[1:2, ]
Y2 <- Y[3:4, ]
result <- nmf.sem(Y1, Y2, rank = 2, maxit = 500)
dot <- nmf.sem.DOT(result)
cat(dot)
#> digraph NMF_SEM_Full_Mechanism {
#>   graph [rankdir=LR compound=true];
#>   splines=true; nodesep=0.4; ranksep=0.7; fontname="Arial";
#> 
#>   // Exogenous variables (Y2)
#>   subgraph cluster_Y2{label="Exogenous (Y2)" style="rounded" color="black" penwidth=1.0;
#>   node [shape=box, style="filled,rounded", fillcolor="lightcoral", color=black, penwidth=1.5];
#>     Y2_1 [label="Petal.Length"];
#>   }
#> 
#>   // Endogenous variables (Y1)
#>   subgraph cluster_Y1{label="Endogenous (Y1)" style="rounded" color="black" penwidth=1.0;
#>   node [shape=box, style="filled,rounded", fillcolor="lightblue", color=black, penwidth=1.5];
#>     Y1_1 [label="Sepal.Length"];
#>     Y1_2 [label="Sepal.Width"];
#>   }
#> 
#>   // Latent Factors (F)
#>   subgraph cluster_F{label="Latent Factors" style="rounded" color="black" penwidth=1.0;
#>   node [shape=ellipse, style="filled,rounded", fillcolor="wheat", color=black, penwidth=1.0];
#>     F_1 [label="Factor 1"];
#>     F_2 [label="Factor 2"];
#>   }
#> 
#>   edge [fontname="Arial", fontsize=8, arrowhead=open];
#> 
#>   // 1. External Driving (Y2 -> Factor) [C2]
#>   edge [color=black, fontcolor=black, style=solid];
#>   Y2_1 -> F_1 [label="0.15", penwidth=5.00];
#> 
#>   // 2. Generation (Factor -> Y1) [X]
#>   edge [color="gray0", fontcolor="gray0", style=solid];
#>   F_1 -> Y1_1 [label="0.99", penwidth=5.00];
#>   F_2 -> Y1_2 [label="0.99", penwidth=5.00];
#> 
#>   // 3. Internal Feedback (Y1 -> Factor) [C1]
#>   edge [style=dashed, color="gray0", fontcolor="gray0"];
#>   Y1_1 -> F_1 [label="0.74", penwidth=3.87];
#>   Y1_2 -> F_1 [label="0.31", penwidth=1.65];
#>   Y1_1 -> F_2 [label="0.02", penwidth=0.50];
#>   Y1_2 -> F_2 [label="0.96", penwidth=5.00];
#> }