similarity {ENMeval} R Documentation

## Calculate Multivariate Environmental Similarity

### Description

NOTICE: This function was borrowed from the rmaxent package written by John Baumgartner (https://github.com/johnbaums/rmaxent/).

Calculate Multivariate Environmental Similarity and most dissimilar/similar variables with respect to a reference dataset, for a set of environmental variables.

### Usage

```similarity(x, ref, full = FALSE)
```

### Arguments

 `x` a 'Raster*', 'list', 'matrix', or 'data.frame' where each layer/column/element represents focal values of an environmental variable. `ref` a 'list', 'matrix', or 'data.frame' where each column/element represents reference values for an environmental variable (corresponding to those given in 'x'). `full` (logical) should similarity values be returned for all variables? If 'FALSE' (the default), then only the minimum similarity scores across variables will be returned.

### Details

'similarity' uses the MESS algorithm described in Appendix S3 of Elith et al. 2010.

### Value

If 'x' is a 'Raster*' object, this function returns a list containing: - 'similarity': a 'RasterStack' giving the environmental similarities for each variable in 'x' (only included when 'full=TRUE'); - 'similarity_min': a 'Raster' layer giving the minimum similarity value across all variables for each location (i.e. the MESS); - 'mod': a factor 'Raster' layer indicating which variable was most dissimilar to its reference range (i.e. the MoD map, Elith et al. 2010); and - 'mos': a factor 'Raster' layer indicating which variable was most similar to its reference range.

If 'x' is a 'list', 'matrix', or 'data.frame', the function will return a list as above, but with 'RasterStack' and 'Raster' objects replaced by matrix and vectors.

### References

Elith, J., Kearney, M., and Phillips, S. (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1: 330-342. doi: 10.1111/j.2041-210X.2010.00036.x

### Examples

```library(dismo)
library(raster)
ff <- list.files(system.file('ex', package='dismo'), '\\.grd\$',
full.names=TRUE )
predictors <- stack(grep('biome', ff, value=TRUE, invert=TRUE))