Description Usage Arguments Details Version Date submitted Data type Author(s) See Also
Model module to fit MaxEnt models using the maxnet package
1 2 |
.df |
Internal parameter, do not use in the workflow function.
|
features |
A length-one character vector (i.e. a string) defining the types of features to use for all covariates (see details). |
regmult |
A positive scalar giving the multiplier for the degree of regularisation. Higher values mean more regularisation. |
clamp_predictions |
Whether to clamp predictions when extrapolating. |
prediction_type |
The scale on which to make predictions (see details for options). |
The maxnet R package fits MaxEnt models using the glmnet package, which enables efficient fitting of glms with regularization. Unlike MaxEnt, MaxNet does not require the user to download and install the MaxEnt java executable.
features
should be a string including an 'l' for linear features, 'q'
for quadratic features, 'h' for hinge features, 'p' for pairwise
interactions and 't' for threshold features. E.g. to use only linear
features and their interactions, either features = 'lp'
or
features = 'pl'
would work. the default value of 'default'
uses maxnet's default settings, adjusting the set up based on the number of
occurrence records np: 'l' if np < 10; 'lq' if np < 15; 'lqh' if np <
80; or 'lqph' if np >= 80. I.e. the default never uses threshold features.
prediction_type
corresponds to the type
argument in the
maxnet
predict function. maxnet
enables types: 'link',
'exponential', 'cloglog' and 'logistic'. However most output modules expect
predictions to be made on the probability scale, for which only 'cloglog'
and 'logistic' are guaranteed to work.
1.0
2016-12-21
presence/background
Nick Golding, nick.golding.research@gmail.com
Other model: BiomodModel
,
Domain
, GBM
,
LogisticRegression
,
MachineLearn
, MaxEnt
,
MaxLike
, MyMaxLike
,
NullModel
, OptGRaF
,
QuickGRaF
, RandomForest
,
StochasticLogisticRegression
,
mgcv
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