TN_plot | R Documentation |
This function visualizes the trait network graph generated by the TN
function.
TN_plot(graph, style = 1, vertex.size = 20, vertex.label.cex = 0.6)
graph |
An igraph object representing the trait network. |
style |
A numeric value that determines the plotting style (default is 1). |
vertex.size |
Numeric value for the size of vertices in the plot (default is 20). |
vertex.label.cex |
Numeric value for the scaling factor of vertex labels (default is 0.6). |
The function uses the cluster_fast_greedy
algorithm to identify communities
in the graph and assigns community membership to vertices. It offers two
plotting styles:
Style 1: Plots the community structure.
Style 2: Plots the graph in a circular layout with vertex colors representing communities. The vertex size and label size can be customized using vertex.size and vertex.label.cex parameters respectively.
An object of class igraph
. This function generates a visualization of the trait network graph.
When style = 1, it displays a community structure plot.
When style = 2, it displays a circular layout plot where vertex colors represent community membership,
edge thickness represents correlation strength, and edge color represents the sign of the correlation (black for positive, red for negative).
He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003
Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with speciesārichness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009
data(PFF)
PFF_traits <- PFF[, c("Height", "Leaf_area","LDMC","SLA","SRL","SeedMass","FltDate",
"FltDur","Leaf_Cmass","Leaf_Nmass","Leaf_CN","Leaf_Pmass",
"Leaf_NP","Leaf_CP","Root_Cmass","Root_Nmass","Root_CN")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05)
TN_plot(Tn_result, style = 1, vertex.size = 20, vertex.label.cex = 0.6)
TN_plot(Tn_result, style = 2, vertex.size = 20, vertex.label.cex = 0.6)
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