MultiTraits (version 0.5.0)

TN_corr: Calculate and Visualize Plant Trait Correlation Network

Description

This function calculates correlation coefficients for given plant traits and generates a correlation network plot.

Usage

TN_corr(traits_matrix, rThres = 0.2, pThres = 0.05, method = "pearson")

Value

Returns a correlation network plot object.

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. Only correlations with absolute values above this threshold will be displayed in the plot.

pThres

Numeric, threshold for p-value, default is 0.05. Only correlations with p-values below this threshold will be displayed in the plot.

method

Character, specifies the correlation method to use: "pearson" (default) or "spearman".

Details

The function first calculates Pearson correlation coefficients between traits, then adjusts p-values using the FDR method. Finally, it plots the correlation network using the corrplot package. The plot displays only correlations that meet both the correlation coefficient and p-value thresholds.

References

1. 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

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness 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_corr(traits_matrix = PFF_traits, rThres = 0.3, pThres = 0.01,method = "pearson")