Package 'meconetcomp'

Title: Compare Microbial Networks of 'trans_network' Class of 'microeco' Package
Description: Compare microbial co-occurrence networks created from 'trans_network' class of 'microeco' package <https://github.com/ChiLiubio/microeco>. This package is the extension of 'trans_network' class of 'microeco' package and especially useful when different networks are constructed and analyzed simultaneously.
Authors: Chi Liu [aut, cre], Minjie Yao [ctb], Xiangzhen Li [ctb]
Maintainer: Chi Liu <[email protected]>
License: GPL-3
Version: 0.8.0
Built: 2026-05-15 02:47:38 UTC
Source: https://github.com/chiliubio/meconetcomp

Help Index

Assign modules to each network

Description

Calculating modularity of networks and assign the modules to nodes for each network.

Usage

cal_module(
  network_list,
  undirected_method = "cluster_fast_greedy",
  directed_method = "cluster_optimal",
  ...
)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
undirected_method

default "cluster_fast_greedy"; the modularity algorithm for undirected network; see cal_module function of trans_network class for more algorithms.
directed_method

default 'cluster_optimal'; the modularity algorithm for directed network.
...

other parameters (except for method) passed to cal_module function of trans_network class.

Value

list, with module attribute in nodes of each network

Examples

data(soil_amp_network)
soil_amp_network <- cal_module(soil_amp_network)

Calculate network topological property for each network

Description

Calculate the topological properties of all the networks and merge the results into one table.

Usage

cal_network_attr(network_list)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Value

data.frame

Examples

data(soil_amp_network)
test <- cal_network_attr(soil_amp_network)

Calculate the cohesion of samples for each network

Description

The cohesion is a method for quantifying the connectivity of microbial communities <doi:10.1038/ismej.2017.91>. It is defined:

Cjpos=∑i=1nai⋅riˉ∣r>0C_{j}^{pos} = \sum_{i=1}^{n} a_{i} \cdot \bar{r_{i}}_{|r>0}

Cjneg=∑i=1nai⋅riˉ∣r<0C_{j}^{neg} = \sum_{i=1}^{n} a_{i} \cdot \bar{r_{i}}_{|r<0}

where CjposC_{j}^{pos} is the positive cohesion, and CjnegC_{j}^{neg} is the negative cohesion. aia_{i} is the relative abundance of species i in sample j. riˉ∣r>0\bar{r_{i}}_{|r>0} denotes the mean weight (correlation coefficient, interaction strength) of all the edges (related with species i) with positive association.

Methods

Public methods

Method new()

Usage
cohesion$new(network_list)
Arguments
network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Returns

res_list, stored in the object. It includes two tables: res_feature and res_sample. In res_feature, the r_pos and r_neg columns mean the riˉ∣r>0\bar{r_{i}}_{|r>0} and riˉ∣r<0\bar{r_{i}}_{|r<0}. In res_sample, the c_pos and c_neg columns denote CjposC_{j}^{pos} and CjnegC_{j}^{neg}.

Examples
t1 <- cohesion$new(soil_amp_network)

Method cal_diff()

Differential test.

Usage
cohesion$cal_diff(
  measure = "c_pos",
  method = c("anova", "KW", "KW_dunn", "wilcox", "t.test")[1],
  ...
)
Arguments
measure

default "c_pos"; "c_pos" or "c_neg" in the res_list$sample; "r_pos" or "r_neg" in the res_list$feature.

method

default "anova"; see the following available options:

'anova'

Duncan's multiple range test for anova

'KW'

KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons, see dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum and Signed Rank Tests for all paired groups

't.test'

Student's t-Test for all paired groups

...

parameters passed to cal_diff function of trans_alpha class of microeco package.

Returns

res_diff in object. See the Return of cal_diff function in trans_alpha class of microeco package.

Examples
\donttest{
t1$cal_diff(method = "wilcox")
}

Method plot()

Plot the result.

Usage
cohesion$plot(measure = "c_pos", ...)
Arguments
measure

default "c_pos"; "c_pos" or "c_neg" in the res_list$sample; "r_pos" or "r_neg" in the res_list$feature.

...

parameters pass to plot_alpha function of trans_alpha class of microeco package.

Returns

ggplot.

Examples
\donttest{
t1$plot(boxplot_add = "none", add_sig = TRUE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
cohesion$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `cohesion$new`
## ------------------------------------------------

t1 <- cohesion$new(soil_amp_network)


## ------------------------------------------------
## Method `cohesion$cal_diff`
## ------------------------------------------------


t1$cal_diff(method = "wilcox")


## ------------------------------------------------
## Method `cohesion$plot`
## ------------------------------------------------


t1$plot(boxplot_add = "none", add_sig = TRUE)

Calculate the cohesion of samples for each network

Description

The cohesion is a method for quantifying the connectivity of microbial communities <doi:10.1038/ismej.2017.91>. It is defined:

Cjpos=∑i=1nai⋅riˉ∣r>0C_{j}^{pos} = \sum_{i=1}^{n} a_{i} \cdot \bar{r_{i}}_{|r>0}

Cjneg=∑i=1nai⋅riˉ∣r<0C_{j}^{neg} = \sum_{i=1}^{n} a_{i} \cdot \bar{r_{i}}_{|r<0}

where CjposC_{j}^{pos} is the positive cohesion, and CjnegC_{j}^{neg} is the negative cohesion. aia_{i} is the relative abundance of species i in sample j. riˉ∣r>0\bar{r_{i}}_{|r>0} denotes the mean weight (correlation coefficient, interaction strength) of all the edges (related with species i) with positive association.

Methods

Public methods

Method new()

Usage
cohesionclass$new(network_list)
Arguments
network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Returns

res_list, stored in the object. It includes two tables: res_feature and res_sample. In res_feature, the r_pos and r_neg columns mean the riˉ∣r>0\bar{r_{i}}_{|r>0} and riˉ∣r<0\bar{r_{i}}_{|r<0}. In res_sample, the c_pos and c_neg columns denote CjposC_{j}^{pos} and CjnegC_{j}^{neg}.

Examples
t1 <- cohesionclass$new(soil_amp_network)

Method cal_diff()

Differential test.

Usage
cohesionclass$cal_diff(
  measure = "c_pos",
  method = c("anova", "KW", "KW_dunn", "wilcox", "t.test")[1],
  ...
)
Arguments
measure

default "c_pos"; "c_pos" or "c_neg" in the res_list$sample; "r_pos" or "r_neg" in the res_list$feature.

method

default "anova"; see the following available options:

'anova'

Duncan's multiple range test for anova

'KW'

KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons, see dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum and Signed Rank Tests for all paired groups

't.test'

Student's t-Test for all paired groups

...

parameters passed to cal_diff function of trans_alpha class of microeco package.

Returns

res_diff in object. See the Return of cal_diff function in trans_alpha class of microeco package.

Examples
\donttest{
t1$cal_diff(measure = "c_pos", method = "wilcox")
}

Method plot()

Plot the result.

Usage
cohesionclass$plot(measure = "c_pos", ...)
Arguments
measure

default "c_pos"; "c_pos" or "c_neg" in the res_list$sample; "r_pos" or "r_neg" in the res_list$feature. This argument only takes effect when cal_diff function has not been used (i.e. res_diff is not in the object). For the case that cal_diff function is performed, please change the measure parameter in cal_diff function.

...

parameters pass to plot_alpha function of trans_alpha class of microeco package.

Returns

ggplot.

Examples
\donttest{
t1$plot(boxplot_add = "none", add_sig = TRUE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
cohesionclass$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `cohesionclass$new`
## ------------------------------------------------

t1 <- cohesionclass$new(soil_amp_network)


## ------------------------------------------------
## Method `cohesionclass$cal_diff`
## ------------------------------------------------


t1$cal_diff(measure = "c_pos", method = "wilcox")


## ------------------------------------------------
## Method `cohesionclass$plot`
## ------------------------------------------------


t1$plot(boxplot_add = "none", add_sig = TRUE)

Generate a microtable object with paired nodes distributions of edges across networks

Description

Generate a microtable object with paired nodes distributions of edges across networks. Useful for the edge comparisons across different networks. The return otu_table in microtable object has the binary numbers in which 1 represents the presence of the edge in the corresponding network.

Usage

edge_comp(network_list)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Value

microtable object

Examples

data(soil_amp_network)
test <- edge_comp(soil_amp_network)
# test is a microtable object

Perform the distance distribution of paired nodes in edges across networks.

Description

This class is a wrapper for a series of analysis on the distance values of paired nodes in edges across networks, including distance matrix conversion, the differential test and the visualization.

Methods

Public methods

Method new()

Usage
edge_node_distance$new(
  network_list,
  dis_matrix = NULL,
  label = "+",
  with_module = FALSE,
  module_thres = 2
)
Arguments
network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

dis_matrix

default NULL; the distance matrix of nodes, used for the value extraction; must be a symmetrical matrix (or data.frame object) with both colnames and rownames (i.e. feature names).

label

default "+"; "+" or "-" or c("+", "-"); the edge label used for the selection of edges.

with_module

default FALSE; whether show the module classification of nodes in the result.

module_thres

default 2; the threshold of the nodes number of modules remained when with_module = TRUE.

Returns

data_table, stored in the object

Examples
\donttest{
data(soil_amp_network)
data(soil_amp)
# filter useless features to speed up the calculation
node_names <- unique(unlist(lapply(soil_amp_network, function(x){colnames(x$data_abund)})))
filter_soil_amp <- microeco::clone(soil_amp)
filter_soil_amp$otu_table <- filter_soil_amp$otu_table[node_names, ]
filter_soil_amp$tidy_dataset()
# obtain phylogenetic distance matrix
phylogenetic_distance <- as.matrix(cophenetic(filter_soil_amp$phylo_tree))
# choose the positive labels
t1 <- edge_node_distance$new(network_list = soil_amp_network, 
	 dis_matrix = phylogenetic_distance, label = "+")
}

Method cal_diff()

Differential test across networks.

Usage
edge_node_distance$cal_diff(
  method = c("anova", "KW", "KW_dunn", "wilcox", "t.test")[1],
  ...
)
Arguments
method

default "anova"; see the following available options:

'anova'

Duncan's multiple range test for anova

'KW'

KW: Kruskal-Wallis Rank Sum Test for all groups (>= 2)

'KW_dunn'

Dunn's Kruskal-Wallis Multiple Comparisons, see dunnTest function in FSA package

'wilcox'

Wilcoxon Rank Sum and Signed Rank Tests for all paired groups

't.test'

Student's t-Test for all paired groups

...

parameters passed to cal_diff function of trans_alpha class of microeco package.

Returns

res_diff in object. See the Return of cal_diff function in trans_alpha class of microeco package.

Examples
\donttest{
t1$cal_diff(method = "wilcox")
}

Method plot()

Plot the distance.

Usage
edge_node_distance$plot(...)
Arguments
...

parameters pass to plot_alpha function of trans_alpha class of microeco package.

Returns

ggplot.

Examples
\donttest{
t1$plot(boxplot_add = "none", add_sig = TRUE)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
edge_node_distance$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `edge_node_distance$new`
## ------------------------------------------------


data(soil_amp_network)
data(soil_amp)
# filter useless features to speed up the calculation
node_names <- unique(unlist(lapply(soil_amp_network, function(x){colnames(x$data_abund)})))
filter_soil_amp <- microeco::clone(soil_amp)
filter_soil_amp$otu_table <- filter_soil_amp$otu_table[node_names, ]
filter_soil_amp$tidy_dataset()
# obtain phylogenetic distance matrix
phylogenetic_distance <- as.matrix(cophenetic(filter_soil_amp$phylo_tree))
# choose the positive labels
t1 <- edge_node_distance$new(network_list = soil_amp_network, 
	 dis_matrix = phylogenetic_distance, label = "+")


## ------------------------------------------------
## Method `edge_node_distance$cal_diff`
## ------------------------------------------------


t1$cal_diff(method = "wilcox")


## ------------------------------------------------
## Method `edge_node_distance$plot`
## ------------------------------------------------


t1$plot(boxplot_add = "none", add_sig = TRUE)

Taxonomic sum of linked nodes in edges across networks

Description

Taxonomic sum of linked nodes in edges across networks.

Usage

edge_tax_comp(
  network_list,
  taxrank = "Phylum",
  label = "+",
  rel = TRUE,
  sep = " -- "
)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
taxrank

default "Phylum"; Which taxonomic level is used for the sum of nodes in edges.
label

default "+"; "+" or "-" or c("+", "-"); the edge label used for the selection of edges.
rel

default TRUE; TRUE represents using ratio, the denominator is the number of selected edges; FALSE represents the absolute number of the sum of edges.
sep

default " – "; The separator for two taxonomic names shown in the result.

Value

data.frame

Examples

data(soil_amp_network)
test <- edge_tax_comp(soil_amp_network)
# test is a microtable object

Get edge property table for each network

Description

Get edge property table for each network in the list with multiple networks.

Usage

get_edge_table(network_list)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Value

list, with res_edge_table in each network

Examples

data(soil_amp_network)
soil_amp_network <- get_edge_table(soil_amp_network)

Get node property table for each network

Description

Get node property table for each network in the list with multiple networks.

Usage

get_node_table(network_list, ...)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
...

parameter passed to get_node_table function of trans_network class.

Value

list, with res_node_table in each network

Examples

data(soil_amp_network)
soil_amp_network <- get_node_table(soil_amp_network, node_roles = FALSE)

Introduction to meconetcomp package (https://github.com/ChiLiubio/meconetcomp)

Description

For the detailed tutorial on meconetcomp package, please follow the links:
Online tutorial website: https://chiliubio.github.io/microeco_tutorial/meconetcomp-package.html
Download tutorial: https://github.com/ChiLiubio/microeco_tutorial/releases

Please open the help document by using help function or by clicking the following links collected:
cal_module
cal_network_attr
get_node_table
get_edge_table
node_comp
edge_comp
edge_node_distance
edge_tax_comp
subset_network
subnet_property
robustness
vulnerability
cohesionclass

To report bugs or discuss questions, please use Github Issues (https://github.com/ChiLiubio/meconetcomp/issues). Before creating a new issue, please read the guideline (https://chiliubio.github.io/microeco_tutorial/notes.html#github-issues).

To cite meconetcomp package in publications, please run the following command to get the reference: citation("meconetcomp")

Reference:
Chi Liu, Chaonan Li, Yanqiong Jiang, Raymond Jianxiong Zeng, Minjie Yao, and Xiangzhen Li. 2023. A guide for comparing microbial co-occurrence networks. iMeta. 2(1): e71.

Generate a microtable object with node distributions across networks

Description

Generate a microtable object with node distributions across networks. Useful for the node information comparisons across different networks.

Usage

node_comp(network_list, property = "name")

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
property

default "name"; a colname of res_node_table in each network; the default "name" represents using node presence/absence information in the otu_table of final output, in which 1 represents presence of the node in the corresponding network; For other options (such as degree), the results in the output otu_table are the actual values of res_node_table.

Value

microtable object

Examples

data(soil_amp_network)
test <- node_comp(soil_amp_network)
# test is a microtable object

Calculate robustness across networks.

Description

This class is a wrapper for robustness calculation and visualization.

Methods

Public methods

Method new()

Usage
robustness$new(
  network_list,
  remove_strategy = c("edge_rand", "edge_strong", "edge_weak", "node_rand", "node_hub",
    "node_hubcon", "node_nethub", "node_modhub", "node_con", "node_degree_high",
    "node_degree_low")[1],
  remove_ratio = seq(0, 1, 0.1),
  remove_number = NULL,
  measure = c("Eff", "Eigen", "Pcr")[1],
  run = 10,
  delete_unlinked_nodes = FALSE
)
Arguments
network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

remove_strategy

default "edge_rand";

"edge_rand"

The edges are randomly removed.

"edge_strong"

The edges are removed in decreasing order of weight.

"edge_weak"

The edges are removed in increasing order of weight.

"node_rand"

The nodes are removed randomly.

"node_hub"

The node hubs are randomly removed. The hubs include network hubs and module hubs.

"node_hubcon"

The node hubs and connectors are randomly removed, including network hubs, module hubs and connectors.

"node_nethub"

The network hubs are randomly removed.

"node_modhub"

The module hubs are randomly removed.

"node_con"

The connectors are randomly removed.

"node_degree_high"

nodes are removed in decreasing order of degree.

"node_degree_low"

nodes are removed in increasing order of degree.

remove_ratio

default seq(0, 1, 0.1). Removed ratio of nodes or edges according to the remove_strategy option.

remove_number

default NULL. Fixed number (e.g., 5 or c(1, 5)) instead of the ratio of remove_ratio parameter. When it is provided, the parameter remove_ratio will be disabled.

measure

default "Eff"; network robustness measures.

"Eff"

network efficiency. The average efficiency of the network is defined:

Eff=1N(N−1)∑i≠j∈G1d(i,j)Eff = \frac{1}{N(N - 1)} \sum_{i \neq j \in G}\frac{1}{d(i, j)}

where N is the total number of nodes and d(i,j) is the shortest path between node i and node j. When the weight is found in the edge attributes, d(i,j)d(i,j) denotes the weighted shortest path between node i and node j. For more details, please read the references <doi: 10.1007/s11704-016-6108-z> and <doi: 10.1038/s41598-020-60298-7>.

"Eigen"

natural connectivity <doi: 10.1007/s11704-016-6108-z>. The natural connectivity can be regarded as an average eigenvalue that changes strictly monotonically with the addition or deletion of edges. It is defined:

λˉ=ln⁡(1N∑i=1Neλ i )\bar{\lambda} = \ln(\frac{1}{N} \sum_{i=1}^{N} e^{\lambda~i~})

where λ i \lambda~i~ is the iith eigenvalue of the graph adjacency matrix. The larger the value of λˉ\bar{\lambda} is, the more robust the network is.

"Pcr"

critical removal fraction of vertices (edges) for the disintegration of networks <doi: 10.1007/s11704-016-6108-z> <doi: 10.1103/PhysRevE.72.056130>. This is a robustness measure based on random graph theory. The critical fraction against random attacks is labeled as PcrP_{c}^r. It is defined:

Pcr=1−1⟨k2⟩⟨k⟩−1P_{c}^r = 1 - \frac{1}{\frac{\langle k^2 \rangle}{\langle k \rangle} - 1}

where ⟨k⟩\langle k \rangle is the average nodal degree of the original network, and ⟨k2⟩\langle k^2 \rangle is the average of square of nodal degree.

run

default 10. Replication number of simulation for the sampling method; Only available when remove_strategy = "edge_rand", "node_rand", "node_hub", "node_hubcon", "node_nethub", "node_modhub" or "node_con".

delete_unlinked_nodes

default FALSE; whether delete the nodes without any link when removing edges.

Returns

res_table and res_summary, stored in the object. The res_table is the original simulation result. The Mean and SD in res_summary come from the res_table.

Examples
tmp <- robustness$new(soil_amp_network, remove_strategy = c("edge_rand"), 
  measure = c("Eff"), run = 3, remove_ratio = c(0.1, 0.5, 0.9))

Method plot()

Plot the simulation results.

Usage
robustness$plot(
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  show_point = TRUE,
  point_size = 1,
  point_alpha = 0.6,
  show_errorbar = TRUE,
  errorbar_position = position_dodge(0),
  errorbar_size = 1,
  errorbar_width = 0.1,
  add_fitting = FALSE,
  ...
)
Arguments
color_values

colors used for presentation.

show_point

default TRUE; whether show the point.

point_size

default .3; point size value.

point_alpha

default .6; point alpha value.

show_errorbar

default TRUE; whether show the errorbar by using the SD result.

errorbar_position

default position_dodge(0); Position adjustment, either as a string (such as "identity"), or the result of a call to a position adjustment function.

errorbar_size

default 1; errorbar line size.

errorbar_width

default 0.1; errorbar width.

add_fitting

default FALSE; whether add fitted smooth line. FALSE denotes add line segment among points.

...

parameters pass to ggplot2::geom_line (when add_fitting = FALSE) or ggplot2::geom_smooth (when add_fitting = TRUE).

Returns

ggplot.

Examples
\donttest{
tmp$plot(linewidth = 1)
}

Method clone()

The objects of this class are cloneable with this method.

Usage
robustness$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `robustness$new`
## ------------------------------------------------

tmp <- robustness$new(soil_amp_network, remove_strategy = c("edge_rand"), 
  measure = c("Eff"), run = 3, remove_ratio = c(0.1, 0.5, 0.9))


## ------------------------------------------------
## Method `robustness$plot`
## ------------------------------------------------


tmp$plot(linewidth = 1)

The soil_amp data

Description

The soil_amp data is the 16S rRNA gene amplicon sequencing dataset of Chinese wetland soils. Reference: An et al. 2019 <doi:10.1016/j.geoderma.2018.09.035>; Liu et al. 2022 <10.1016/j.geoderma.2022.115866>

Usage

data(soil_amp)

The soil_amp_network data

Description

The soil_amp_network data is a list storing three trans_network objects created based on soil_amp data. Three networks are created for IW, CW and TW groups, respectively.

Usage

data(soil_amp_network)

The soil_measure_diversity data

Description

The soil_measure_diversity data is a table storing all the abiotic factors and functional diversity based on the metagenomic sequencing and MetaCyc pathway analysis.

Usage

data(soil_measure_diversity)

The stool_met data

Description

The stool_met data is the metagenomic species abundance dataset of stool samples selected from R ExperimentHub package. It has 198 samples, collected from the people with alcohol drinking habit, and 92 species.

Usage

data(stool_met)

Calculate properties of sub-networks selected according to features in samples

Description

Extracting sub-network according to the presence of features in each sample across networks and calculate the sub-network properties.

Usage

subnet_property(network_list, ...)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
...

parameters pass to subset_network function of trans_network class in microeco package (except the node parameter).

Value

data.frame

Examples

data(soil_amp_network)
test <- subnet_property(soil_amp_network)

Extract subset of network according to the edge intersection of networks

Description

Extracting a network according to the edge intersection of networks.

Usage

subset_network(network_list, venn = NULL, name = NULL)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.
venn

default NULL; a microtable object which must be converted by trans_comm function of trans_venn class.
name

default NULL; integer or character; must be a number or one of colnames of the otu_table in the input venn parameter.

Value

trans_network object, with only the extracted edges in the network

Examples

data(soil_amp_network)
# first obtain edge distribution
tmp <- edge_comp(soil_amp_network)
# obtain edge intersection using trans_venn class
tmp1 <- microeco::trans_venn$new(tmp)
# convert intersection result to microtable object
tmp2 <- tmp1$trans_comm()
# extract the intersection of all the three networks ("IW", "TW" and "CW")
test <- subset_network(soil_amp_network, venn = tmp2, name = "IW&TW&CW")
# test is a trans_network object

Calculate the vulnerability of each node for each network

Description

The vulnerability of each node represents the influence of the node on the global efficiency of the network, i.e. the efficiency of network after removing the targeted node. For the detailed defination of global efficiency, please see the "Eff" option of measure parameter in robustness class.

Usage

vulnerability(network_list)

Arguments

network_list

a list with multiple networks; all the networks should be trans_network object created from trans_network class of microeco package.

Value

data.frame

Examples

data(soil_amp_network)
vulnerability_table <- vulnerability(soil_amp_network)