Analysing how network conclusions change when the number of nodes sampled is altered

This package takes matrixes of interactions, which are formatted in columns as 'speciesname-sampleID', then uses a randomly selected subset of a given number of them to make a matrix. The columns of the resulting matrix are then collapsed by species, and your desired metric from the bipartite package is then calculated. As the selection of nodes retained are chosen randomly, it is best to perform multiple iterations of the analysis.

The function used by the user can accept either individual matrixes, or a list of matrixes. If using a list, please ensure your list items are named.

(Dave Hemprich-Bennett, hemprich.bennett@gmail.com, @hammerheadbat)

library(devtools) install_github('hemprichbennett/netReducer') library(netReducer) #Generate two example matrixes to analyse trialmat1 <- matrix(sample(c(0,1),100, replace = T, prob = c(0.75, 0.25), nrow = 10) colnames(trialmat1) <- c('Hice-001', 'Hice-002', 'Rhtr-001', 'Rhtr-008', 'Keha-0005', 'Keha-01A', 'Hice-005', 'Rhtr-010', 'Hidi-001', 'Hice-087') #trialmat1 trialmat2 <- matrix(sample(c(0,1),100, replace = T, prob = c(0.85, 0.15)), nrow = 10) colnames(trialmat2) <- c('Hidy-001', 'Hidy-002', 'Rhtr-001', 'Rhtr-008', 'Keha-0005', 'Keha-01A', 'Hidy-005', 'Rhtr-010', 'Hidi-001', 'Hidy-087') #The matrixes can be analysed individually, or a series of them can be analysed together as a list input_list <- list('trialmat1' = trialmat1, 'trialmat2' = trialmat2) #Here a single matrix is analysed one_net_example <- netreducing(input = trialmat1, input_type = 'matrix', n_iterations = 10, min_nodes = 5, metric_chosen = 'connectance', type_chosen = 'network') #Here multiple matrixes in a list are analysed two_net_example <- netreducing(input = input_list, input_type = 'list', n_iterations = 10, min_nodes = 5, metric_chosen = 'degree', type_chosen = 'species', level = 'higher') #Plot the outputs library(ggplot2) ggplot(two_net_example, aes(x=n_used, y= metricval, colour = netnames))+ geom_point()+ geom_smooth(method = lm)+ labs(x = 'Number of samples used', y = 'Degree')

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