importance {sjSDM} | R Documentation |
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, ...)
Arguments
x |
object fitted by |
save_memory |
use torch backend to calculate importance with single precision floats |
... |
additional arguments |
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.
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
Author(s)
Maximilian Pichler
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
## Not run:
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)
## End(Not run)