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Generate Graphviz DOT Scripts for NMF or NMF-with-Covariates Models

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

Produces a Graphviz DOT script visualizing the structure of an NMF model (\(Y \approx X C A\)) or its simplified forms.

Supported visualization types:

  • "YX" — Standard NMF view: latent factors \(X\) map to observations \(Y\).

  • "YA" — Direct regression view: covariates \(A\) map directly to \(Y\) using the combined coefficient matrix \(X C\).

  • "YXA" — Full tri-factorization: \(A \rightarrow C \rightarrow X \rightarrow Y\).

Edge widths are scaled by coefficient magnitude, and nodes with no edges above the threshold are omitted from the visualization.

Usage

nmfkc.DOT(
  result,
  type = c("YX", "YA", "YXA"),
  threshold = 0.01,
  sig.level = 0.1,
  rankdir = "LR",
  fill = TRUE,
  weight_scale = 5,
  weight_scale_ax = weight_scale,
  weight_scale_xy = weight_scale,
  weight_scale_ay = weight_scale,
  Y.label = NULL,
  X.label = NULL,
  A.label = NULL,
  Y.title = "Observation (Y)",
  X.title = "Basis (X)",
  A.title = "Covariates (A)",
  hide.isolated = TRUE
)

Arguments

result

The return value from nmfkc, containing matrices X, B, and optionally C.

type

Character string specifying the visualization style: one of "YX", "YA", "YXA".

threshold

Minimum coefficient magnitude to display an edge.

sig.level

Significance level for filtering C edges when inference results are available (i.e., x is of class "nmfkc.inference"). Only edges with p-value below sig.level are shown, decorated with significance stars (*, **, ***). Set to NULL to disable filtering and show all edges above threshold. Default is 0.1.

rankdir

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

fill

Logical; whether nodes should be drawn with filled shapes.

weight_scale

Base scaling factor for edge widths.

weight_scale_ax

Scaling factor for edges \(A \rightarrow X\) (type "YXA").

weight_scale_xy

Scaling factor for edges \(X \rightarrow Y\).

weight_scale_ay

Scaling factor for edges \(A \rightarrow Y\) (type "YA").

Y.label

Optional character vector for labels of Y nodes.

X.label

Optional character vector for labels of X (latent factor) nodes.

A.label

Optional character vector for labels of A (covariate) nodes.

Y.title

Cluster title for Y nodes.

X.title

Cluster title for X nodes.

A.title

Cluster title for A nodes.

hide.isolated

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

Value

A character string representing a Graphviz DOT script.

See also

nmfkc, nmfae.DOT, nmf.sem.DOT, nmfkc.ar.DOT, plot.nmfkc.DOT

Examples

Y <- matrix(cars$dist, nrow = 1)
A <- rbind(1, cars$speed)
result <- nmfkc(Y, A, rank = 1)
#> Y(1,50)~X(1,1)C(1,2)A(2,50)=XB(1,50)...
#> 0sec
dot <- nmfkc.DOT(result)
cat(dot)
#> digraph NMF {
#>   graph [rankdir=LR compound=true];
#>   splines=true; nodesep=0.4; ranksep=0.7; fontname="Arial";
#> 
#>   // Output variables (Y)
#>   subgraph cluster_Y{label="Observation (Y)" style="rounded" color="black" penwidth=1.0;
#>   node [shape=box, style="filled,rounded", fillcolor="lightblue", color=black, penwidth=1.5];
#>     Y_1 [label="Y1"];
#>   }
#> 
#>   // Latent factors (X)
#>   subgraph cluster_X{label="Basis (X)" style="rounded" color="black" penwidth=1.0;
#>   node [shape=ellipse, style="filled,rounded", fillcolor="wheat", color=black, penwidth=1.0];
#>     X_1 [label="Basis1"];
#>   }
#> 
#>   edge [fontname="Arial", fontsize=8, arrowhead=open];
#> 
#>   // X -> Y edges (X)
#>   edge [color="gray0", fontcolor="gray0", style=solid];
#>   X_1 -> Y_1 [label="1.00", penwidth=5.00];
#> }