randomized_ranges: Identify meaningful trends emerging from MOTU clustering...

In hemprichbennett/lOTUs: Showing the effects of clustering on bipartite network data

Description

Acts as a wrapper for the metcalcs function, randomising a networks across a series of clustering levels

Usage

randomized_ranges(networks, input_format = "clust_net", indices,
  network_level = "both", n_perm = 1000, sums_to_preserve = "both",
  summarise = T, quantiles_to_return = c(0.025, 0.975),
  out_format = "data.frame", actual_vals = F)

Arguments

networks	A nested list of networks
input_format	The input must be a list of networks, either a single network identity generated at different clustering levels ('clust_only'), or a nested list of networks, with the levels either being clustering, then network identity ('clust_net'), or network identity, then clustering ('net_clust').
indices	A vector of indices to be calculated. See the functions networklevel and computeModules in package bipartite for details
network_level	The network level to analyse
n_perm	The number of permutations to run
sums_to_preserve	The sums to be preserved in randomisation. Possible values are 'none', 'rows', 'columns', or 'both'
summarise	Should the randomised values be summarised to the upper and lower quantiles, or should all randomised values be returned?
quantiles_to_return	The quantiles desired if summarising the output
out_format	The format for the data to be output in. Either a dataframe ('data.frame') or list ('list')
actual_vals	Should the actual values for each network be calculated?

Value

Produces either a dataframe or list for your desired metrics and randomisations

See Also

metcalcs

hemprichbennett/lOTUs documentation built on Jan. 22, 2021, 8:54 p.m.