NetworkEcoSeries: Networkeco for data series
In EcotoneFinder: Characterising and Locating Ecotones and Communities

Description Usage Arguments Details Value Examples

Networkeco for data series

NetworkEcoSeries(ecotonefinder, threshold = 0.8, method = c("cmeans",
  "vegclust"), plot.type = c("percentage", "heatmap", "corrplot",
  "network"), plot = c("species", "community"), no.plot = FALSE,
  order.sp = NULL, dist.method = "inner_product", dist = c("count",
  "relative", "raw"), network.group = c("site", "cluster"),
  method.corr = "number", ...)

`ecotonefinder`	A list containing elements named in the same way than EcotoneFinderSeries function outcomes. Must contain cmeans results or vegclust results.
`threshold`	If dist = "count", the membership grade threshold used to sort the species in the different clusters.
`method`	The membership computation method to be used. Must be present in the ecotonefinder list.
`plot.type`	Which graphical representation to be plotted. Among "percentage", "corrplot", "heatmap","network"
`plot`	If plot = "species", the distances are computed between the species in the data. If plot = "community", the distances are computed between the cluster centroids.
`no.plot`	Logical. Should the plot be displayed?. Set to TRUE to gain computation time with large community matrix.
`order.sp`	Vector providing the order in which to arrange the species. If NULL, the column order will be kept.
`dist.method`	Distance method for the computation of a distance matrix, when dist = "raw" or dist = "percent".
`dist`	The type of data on which distance calculations are made from. If dist = "raw", the distance matrix is computed from the membership matrix directly. if dist = "relative", the distance matrix is computed from the relative memberships grades of each species in the clusters (between 0 and 1). If dist = "count", the species are assigned to clusters according to the threshold and the distance matrix is computed from the number of common species between the different clusters. See details.
`network.group`	If network.group = "site" the nodes of the networks will be colored according to the different times or sites of the series. If network.group = "cluster" the nodes of the network will be colored according to the different fuzzy clusters. Can be user defined (see qgraph documentation for details) but must be a factor of the same lenght as the nodes of the graph.
`method.corr`	If plot.type = "corrplot", the method to be used for the corrplot. Must be one of "circle", "square", "ellipse", "number", "shade", "color", "pie". Default to "number".
`...`	Additional arguments to be passed to the plotting functions, see details.

NetworkEcoSeries is a generalisation of the NetworkEco function to analyses space/time series. The ... argument may be used to pass additional arguments to the plotting functions (for graphical purposes).

A list containing the percentage matrix, the distance matrix and the network object (depending of the arguments passed to the function)

  
  SyntheticTrialSeries <- SyntheticDataSeries(CommunityPool = 40,
                                              CommunityNum = 4, SpCo = NULL,
                                              Length = 500, SeriesNum = 5,
                                              Parameters = list(a=rep(60, 4),
                                                              b=c(0,200,350,500),
                                                              c=rep(0.03,4)),
                                              pal = c("#008585", "#B8CDAE", "#E6C186", "#C7522B"),
                                              replacement = TRUE,
                                              Parameters.repl = TRUE)

  EcoTimeSeriesTrial <- EcotoneFinderSeries(data = SyntheticTrialSeries,
                                            dist = "Distance",
                                            method = c("cmeans","vegclust"),
                                            series = "Time", groups = 4,
                                            standardize = "hellinger", na.rm=TRUE)

#### Network from the common number of species above membership threshold between clusters:
  SyntheticNetworkSeries <- NetworkEcoSeries(EcoTimeSeriesTrial, threshold = .2,
                                             method = "cmeans", plot.type = "network",
                                             plot = "community", dist = "count",
                                             network.group = "cluster",
                                             dist.method = "inner_product",
                                             no.plot = FALSE, layout = "spring",
                                             shape = "ellipse",
                                             palette = "colorblind")

#### Network of relations between species from their raw membership values in each cluster:
  SyntheticNetworkSeries <- NetworkEcoSeries(EcoTimeSeriesTrial, threshold = .2,
                                             method = "cmeans", plot.type = "network",
                                             plot = "species", dist = "raw",
                                             dist.method = "inner_product",
                                             no.plot = FALSE, layout = "spring",
                                             shape = "ellipse",
                                             palette = "colorblind")