sdm (version 1.2-32)

getVarImp: variable importance

Description

Calculates relative importance of different variables in the models using several approaches.

Usage

getVarImp(x,id,wtest,setting,...)

Arguments

x

sdmModels object

id

numeric, specify the model (modelID) for which the variable importance values are extracted; OR it can be character with "ensemble" specifying that the variable importance should be calculated based on the ensemble of all the model objects

wtest

specifies which dataset ('training','test.dep','test.indep') should be used (if exist) to calculate the importance of variables

setting

an optional list with setting of ensemble function; it is only needed when id = 'ensemble'

...

additional arguments as for getModelId function including species,method, replication, and run

Author

Babak Naimi

https://www.r-gis.net/

https://www.biogeoinformatics.org/

Details

getVarImp function returns an object including different measures of variable importance, and if be put in plot function, a barplot is generated. If the ggplot2 package is installed on your machine, the plot is generated using ggplot (unless you turn gg = FALSE), otherwise, the standard barplot is used.

if id = "ensemble" is used in the function, the ensemble function is called to calculate the relative variable importance based on the ensemble prediction of all models. setting can be specified as an additional argument that will be passed to the ensemble function so check the ensemble function to see how can setting be specified!

References

Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881

Examples

if (FALSE) {
# if m is a sdmModels object (output of the sdm function) then:

getVarImp(m,id=1) # variable importance 

vi <- getVarImp(m,id=1) 

vi

plot(vi,'auc')

plot(vi,'cor')
#############
# You can get Mean variable importance (and confidence interval) for multiple models:

vi <- getVarImp(m,id=1:10) # specify the modelIDs of the models

vi

plot(vi,'cor')
#----
# you can use the getModelId function to find the id of the specific method, replication, etc.
# or you may put the arguments of the getModelId in the getVarImp function:

vi <- getVarImp(m, method='glm') # Mean variable importance for the method glm

vi

plot(vi)
#################

##### Variable Importance based on ENSEMBLE:

# You can get variable importance based on the ensemble of multiple models:

# setting is passed to the ensemble function

vi <- getVarImp(m,id="ensemble",setting=list(method='weighted',stat='auc'))

vi

plot(vi,'cor')

#----------------

# if you want the ensemble based on a subset of models, you can define
# the id within the setting list:

vi <- getVarImp(m,id="ensemble",
        setting=list(method='weighted',stat='auc',id=1:10))

vi

plot(vi,'cor')


plot(vi, gg = F) # R standard plot is used instead of ggplot


}