TN {MultiTraits} | R Documentation |
Generate Plant Trait Network
Description
This function creates a network graph from a plant trait correlation matrix, applying thresholds for correlation strength and significance.
Usage
TN(traits_matrix, rThres = 0.2, pThres = 0.05, method = "pearson")
Arguments
traits_matrix |
A numeric matrix where each column represents a plant trait and each row represents a sample. |
rThres |
Numeric, threshold for correlation coefficient, default is 0.2. Correlations with absolute values below this threshold are set to zero. |
pThres |
Numeric, threshold for p-value, default is 0.05. Only correlations with p-values below this threshold are included in the network. |
method |
Character, specifies the correlation method to use: "pearson" (default) or "spearman". |
Details
The function performs the following steps:
Calculates Pearson correlation coefficients and p-values for the trait matrix.
Applies correlation coefficient and p-value thresholds to filter relationships.
Constructs a weighted undirected graph from the filtered correlation matrix.
Removes self-loops and isolated nodes from the graph.
Adds correlation coefficients as edge attributes.
Value
Returns an igraph object representing the trait network.
References
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
Examples
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, method = "pearson")
Tn_result