EcotoneFinder (version 0.2.3)

NetworkEcoSeries: Networkeco for data series

Description

Networkeco for data series

Usage

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", ...)

Arguments

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.

Value

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

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).

Examples

# NOT RUN {
  
# }
# NOT RUN {
  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")
 
# }
# NOT RUN {
# }