Plot graph of direct species associations.

Plot graph of direct species associations.

Usage

# S3 method for cgr
plot(
  x,
  P = NULL,
  vary.edge.lwd = FALSE,
  edge.col = c("light blue", "pink"),
  label = colnames(x$obj$fitted),
  vertex.col = "blue",
  label.cex = 0.8,
  edge.lwd = ifelse(vary.edge.lwd, 10, 4),
  edge.lty = c(1, 1),
  ...
)

Arguments

x

is a cgr object, e.g. from output of cgr.

P

locations of graph nodes, if NULL (default) these are generated with a Fruchterman Reingold algorithm.

vary.edge.lwd

is logical, TRUE will vary line width according to the strength of partial correlation, default (FALSE) uses fixed line width.

edge.col

takes two colours as arguments - the first is the colour used for positive partial correlations, the second is the colour of negative partial correlations.

label

is a vector of labels to apply to each variable, defaulting to the column names supplied in the data.

vertex.col

the colour of graph nodes.

label.cex

is the size of labels.

edge.lwd

is line width, defaulting to 10*partial correlation when varying edge width, and 4 otherwise.

edge.lty

is a vector of two integers specifying the line types for positive and negative partial correlations, respectively. Both default to solid lines.

...

other parameters to be passed through to plotting gplot, in particular pad, the amount to pad the plotting range is useful if labels are being clipped. For details see the gplot help file.

Value

a plot of species associations after accounting for the effect of all other species, positive/negative are blue/pink. The matrix of node positions (P) is returned silently.

See also

gplot, cgr

Examples

abund <- spider$abund[,1:5]
spider_mod <- stackedsdm(abund,~1, data = spider$x, ncores=2) 
spid_graph=cgr(spider_mod)
plot(spid_graph, edge.col=c("forestgreen","darkorchid4"), 
                 vertex.col = "black",vary.edge.lwd=TRUE)

                 
# \donttest{
library(tidyr)
library(tidygraph)
#> 
#> Attaching package: ‘tidygraph’
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
library(ggraph)
#> Loading required package: ggplot2

igraph_out<-spid_graph$best_graph$igraph_out

igraph_out %>% ggraph('fr') + # see ?layout_tbl_graph_igraph
   geom_edge_fan0(aes( colour = partcor, width=partcor)) +
   scale_edge_width(range = c(0.5, 3))+
   scale_edge_color_gradient2(low="#b2182b",mid="white",high="#2166ac")+
   geom_node_text(aes(label=name), repel = TRUE)+
   geom_node_point(aes(size=1.3))+
   theme_void() +
   theme(legend.position = 'none')

# }