line_plot: Returns a figure showing how conclusions could change over...

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

Description

Returns a figure showing how conclusions could change over MOTU clustering thresholds. Nb this function assumes that all datasets analysed using metcalcs were generated with the exact same clustering thresholds

Usage

line_plot(input, network, clustering, metric, value, plotname = NULL,
  colour = F, palette, x_lab = "Clustering (%)", vertical_lines = NA)

Arguments

input	An input data frame, as output fully-formatted by the metcalcs function
network	The network column of the data frame
clustering	The clustering level column of the data frame
metric	The column of the data frame containing the metrics desired for analysis
value	The column of the data frame containing the values obtained for each metric
plotname	A title for the plot, defaults to none
colour	Should the plot be in colour?
palette	A palette of colours to plot
x_lab	The label to be used on the x-axis
vertical_lines	an integer or vector of integers stating where to locate vertical guide-lines on the plot

Value

Produces a simple plot showing which metrics are robust in your dataset to clustering-level effects

See Also

metcalcs which this function visualises the output of

Examples

m <- metcalcs(networks= batnets, indices =c('functional complementarity','web asymmetry', 'Fisher alpha'), network_level = 'higher')
line_plot(input = m, metric = 'metric', network = 'network', clustering = 'clustering', value = 'value', plotname = 'Sabah dataset')

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