anova.sjSDM: Anova / Variation partitioning

Description

Compute variance explained by the three fractions env, space, associations

Usage

# S3 method for sjSDM
anova(object, samples = 5000L, verbose = TRUE, ...)

Value

An S3 class of type 'sjSDManova' including the following components:

results: Data frame of results.
to_print: Data frame, summarized results for type I anova.
N: Number of observations (sites).
spatial: Logical, spatial model or not.
species: individual species R2s.
sites: individual site R2s.
lls: individual site by species negative-log-likelihood values.
model: model

Implemented S3 methods are print.sjSDManova and plot.sjSDManova

Arguments

object: model of object sjSDM
samples: Number of Monte Carlo samples
verbose: TRUE or FALSE, indicating whether progress should be printed or not
...: optional arguments which are passed to the calculation of the logLikelihood

Details

The ANOVA function removes each of the three fractions (Environment, Space, Associations) and measures the drop in variance explained, and thus the importance of the three fractions.

Variance explained is measured by Deviance as well as the pseudo-R2 metrics of Nagelkerke and McFadden

In downstream functions such as plot.sjSDManova or plot.sjSDManova with add_shared=TRUE. The anova can get unstable for many species and few occurrences/observations. We recommend using large numbers for 'samples'.

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))))
}