internalStructure: Plot internal metacommunity structure

Description

Plot internal metacommunity structure

Usage

internalStructure(
  object,
  Rsquared = c("McFadden", "Nagelkerke"),
  fractions = c("discard", "proportional", "equal"),
  negatives = c("floor", "scale", "raw"),
  plot = FALSE
)

Value

An object of class sjSDMinternalStructure consisting of a list of data.frames with the internal structure.

Arguments

object

anova object from anova.sjSDM

Rsquared

which R squared should be used, McFadden or Nagelkerke (McFadden is default)

fractions

how to handle shared fractions

negatives

how to handle negative R squareds

plot

should the plots be suppressed or not.

Plots and returns the internal metacommunity structure of species and sites (see Leibold et al., 2022). Plots were heavily inspired by Leibold et al., 2022

References

Leibold, M. A., Rudolph, F. J., Blanchet, F. G., De Meester, L., Gravel, D., Hartig, F., ... & Chase, J. M. (2022). The internal structure of metacommunities. Oikos, 2022(1).

Examples

Run this code

if (FALSE) {
library(sjSDM)
# simulate community:
community = simulate_SDM(env = 3L, species = 10L, sites = 100L)

Occ <- community$response
Env <- community$env_weights
SP <- data.frame(matrix(rnorm(200, 0, 0.3), 100, 2)) # spatial coordinates


# fit model:
model <- sjSDM(Y = Occ, 
               env = linear(data = Env, formula = ~X1+X2+X3), 
               spatial = linear(data = SP, formula = ~0+X1*X2), 
               family=binomial("probit"),
               verbose = FALSE,
               iter = 20) # increase iter for real analysis

# Calculate ANOVA for env, space, associations, for details see ?anova.sjSDM
an = anova(model, samples = 10, verbose = FALSE) # increase iter for real analysis

# Show anova fractions
plot(an)

# ANOVA tables with different way to handle fractions
summary(an)
summary(an, fractions = "discard")
summary(an, fractions = "proportional")
summary(an, fractions = "equal")

# Internal structure
int = internalStructure(an, fractions = "proportional")

print(int)

plot(int) # default is negative values will be set to 0
plot(int, negatives = "scale") # global rescaling of all values to range 0-1
plot(int, negatives = "raw") # negative values will be discarded

plotAssemblyEffects(int)
plotAssemblyEffects(int, negatives = "floor")
plotAssemblyEffects(int, response = "sites", pred = as.factor(c(rep(1, 50), rep(2, 50))))
plotAssemblyEffects(int, response = "species", pred = runif(10))
plotAssemblyEffects(int, response = "species", pred = as.factor(c(rep(1, 5), rep(2, 5))))
}