Add data-driven edge and node weights to the input graph.
weightGraph(graph, data, group = NULL, method = "r2z", limit = 10000, ...)An igraph object.
A matrix or data.frame. Rows correspond to subjects, and columns to graph nodes.
Binary vector. This vector must be as long as the number
of subjects. Each vector element must be 1 for cases and 0 for control
subjects. By default, group = NULL. If group is not NULL, also
node weighting is actived, and node weights correspond to the attribute:
V(graph)$pv (P-value of the z-test = b/SE(b) from simple linear regression
y ~ x, i.e., lm(node ~ group)) and V(graph)$sign (-1 if z<-2, +1 if z>2,
0 otherwise).
Edge weighting method. It can be one of the following:
"r2z", weight edges are defined using Fisher's r-to-z transform
(Fisher, 1915) to test the correlation coefficient of pairs of interacting
nodes, if group=NULL. Otherwise, the difference between group of
the r-to-z trasform will be tested. Edge weights correspond to the attribute:
E(graph)$pv (P-value of the z-test) and E(graph)$sign (-1 if z<-2, +1 if z>2,
0 otherwise).
"sem", edge weights are defined by a SEM model that implies
testing the group effect simultaneously on source and sink nodes.
A new parameter w is defined as the weighted sum of the total effect
of the group on source and sink nodes, adjusted by node degree centrality.
Edge weights correspond to the attribute: E(graph)$pv (P-value of the
z-test = w/SE(w)) and E(graph)$sign (-1 if z<-2, +1 if z>2, 0 otherwise).
Not available if group=NULL.
"cov", edge weights are defined by a new parameter w combining
the group effect on the source node (mean group difference, adjusted
by source degree centrality), the sink node (mean group difference,
adjusted by sink degree centrality), and the source--sink interaction
(correlation difference). Edge weights correspond to the attribute:
E(graph)$pv (P-value of the z-test = w/SE(w) of the combined difference
of the group over source node, sink node, and their connection) and
E(graph)$sign (-1 if z<-2, +1 if z>2, 0 otherwise).
Not available if group=NULL.
"cfa", edge weights are defined by a CFA1 model that implies
testing the group effect, w on a latent variable (LV) with observed
indicators two interacting nodes, fixing loading coefficients and residual
variances for model identification. Edge weights correspond to the
attribute: E(graph)$pv (P-value of the z-test = w/SE(w) of the group
effect on the LV) and E(graph)$sign (-1 if z<-2, +1 if z>2, 0 otherwise).
Not available if group=NULL.
An integer value corresponding to the number of graph
edges. Beyond this limit, multicore computation is enabled to reduce
the computational burden. By default, limit = 10000.
Currently ignored.
A weighted graph, as an igraph object.
Grassi M, Tarantino B (2023). [Supplementary material of] SEMtree: tree-based structure learning methods with structural equation models. Bioinformatics, 39 (6), 4829–4830 <https://doi.org/10.1093/bioinformatics/btad377>
Fisher RA (1915). Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika, 10(4), 507–521. <doi:10.2307/2331838>
# Graph weighting
G <- weightGraph(graph = sachs$graph,
data = log(sachs$pkc),
group = sachs$group,
method = "r2z")
# New edge attributes
head(E(G)$pv); summary(E(G)$pv)
#> [1] 2.661577e-01 8.370494e-01 5.528435e-01 4.401098e-01 4.742076e-01
#> [6] 1.003647e-09
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0000000 0.0000004 0.2412998 0.2952637 0.5031423 0.9642826
head(E(G)$zsign); table(E(G)$zsign)
#> [1] 0 0 0 0 0 1
#>
#> 0 1
#> 13 7
# New node attributes
head(V(G)$pv); summary(V(G)$pv)
#> [1] 1.721660e-35 1.576567e-06 2.219336e-38 7.635299e-22 8.952626e-95
#> [6] 5.688104e-01
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0000000 0.0000000 0.0000000 0.1076364 0.0000008 0.6151884
head(V(G)$zsign); table(V(G)$zsign)
#> [1] 1 1 -1 -1 -1 0
#>
#> -1 0 1
#> 3 2 6