sjSDM (version 1.0.6)

importance: Importance of environmental, spatial and association components

Description

Computes standardized variance components with respect to abiotic, biotic, and spatial effect groups.

Usage

importance(x, save_memory = TRUE, ...)

Value

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

names

Character vector, species names.

res

Data frame of results.

spatial

Logical, spatial model or not.

Implemented S3 methods include print.sjSDMimportance and plot.sjSDMimportance

Arguments

x

object fitted by sjSDM or a list with beta, the association matrix, and the correlation matrix of the predictors, see details below

save_memory

use torch backend to calculate importance with single precision floats

...

additional arguments

Author

Maximilian Pichler

Details

This approach is based on Ovaskainen et al., 2017, and also used in Leibold et al., 2021. Unlike the anova.sjSDM function in the sjSDM package, importance is not calculated by explicitly switching a particular model component of and refitting the model, but essentially by setting it ineffective.

Although we have no hard reasons to discourage the use of this function, we have decided in sjSDM to measure importance maninly based on a traditional ANOVA approach. We therefore recommend users to use the anova.sjSDM.

This function is maintained hidden for comparison / benchmarking purpose, and in case there is a need to use it in the future. If you want to access it, use sjSDM:::importance.

References

Ovaskainen, O., Tikhonov, G., Norberg, A., Guillaume Blanchet, F., Duan, L., Dunson, D., ... & Abrego, N. (2017). How to make more out of community data? A conceptual framework and its implementation as models and software. Ecology letters, 20(5), 561-576.

Leibold, M. A., Rudolph, F. J., Blanchet, F. G., De Meester, L., Gravel, D., Hartig, F., ... & Chase, J. M. (2021). The internal structure of metacommunities. Oikos.

See Also

print.sjSDMimportance, plot.sjSDMimportance

Examples

if (FALSE) {
library(sjSDM)
com = simulate_SDM(sites = 300L, species = 12L, 
                   link = "identical", response = "identical")
Raw = com$response
SP = matrix(rnorm(300*2), 300, 2)
SPweights = matrix(rnorm(12L), 1L)
SPweights[1,1:6] = 0
Y = Raw + (SP[,1,drop=FALSE]*SP[,2,drop=FALSE]) %*% SPweights
Y = ifelse(Y > 0, 1, 0)

model = sjSDM(Y = Y,env = linear(com$env_weights, lambda = 0.001), 
              spatial = linear(SP,formula = ~0+X1:X2, lambda = 0.001), 
              biotic = bioticStruct(lambda = 0.001),iter = 40L, verbose = FALSE)
imp = importance(model)
plot(imp)
}