Function to create an ensemble of partial effects from multiple modelsSource:
Similar to the
ensemble() function, this function creates an
ensemble of partial responses of provided distribution models fitted with
ibis.iSDM-package. Through the
layer parameter it can be
specified which part of the partial prediction should be averaged in an
ensemble (if given). This can be for instance the mean prediction and/or
the standard deviation sd. Ensemble partial is also being called if more
than one input
DistributionModel object is provided to
By default the ensemble of partial responses is created as average across all models with the uncertainty being the standard deviation of responses.
# S4 method for ANY,character,character,character,ANY,logical ensemble_partial(...,x.var,method,layer,newdata,normalize)
DistributionModelobjects from which partial responses can be called. In the future provided data.frames might be supported as well.
characterof the variable from which an ensemble is to be created.
Approach on how the ensemble is to be created. See details for options (Default:
characterof the layer to be taken from each prediction (Default:
'mean'). If set to
NULLignore any of the layer names in ensembles of
logicalon whether the inputs of the ensemble should be normalized to a scale of 0-1 (Default:
A data.frame with the combined partial effects of the supplied models.
Possible options for creating an ensemble includes:
'mean'- Calculates the mean of several predictions.
'median'- Calculates the median of several predictions.
If a list is supplied, then it is assumed that each entry in the list
is a fitted
DistributionModel object. Take care not to create an
ensemble of models constructed with different link functions, e.g. logistic
vs log. By default the response functions of each model are normalized.