Description Usage Arguments Details Author(s) References Examples
Calculates relative importance of different variables in the models using several approaches.
1 |
x |
sdmModels object |
id |
numeric, specify the model (modelID) for which the variable importance values are extracted |
wtest |
specifies which dataset ('training','test.dep','test.indep') should be used (if exist) to calculate the importance of variables |
... |
additional arguments as for |
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.
Babak Naimi naimi.b@gmail.com
https://www.biogeoinformatics.org
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, DOI: 10.1111/ecog.01881
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | ## Not run:
# if m is a sdmModels object (output of the sdm function) then:
getVarImp(m,id=1,wtest='training') # variable importance based on training dataset
vi <- getVarImp(m,id=1,wtest='test.dep')
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,wtest='test.dep') # 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)
plot(vi, gg = F) # R standard plot is used instead of ggplot
## End(Not run)
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