Description Usage Arguments Details Value Author(s) See Also Examples
ame
estimates average marginal effects (AME) for models fitted using
different machine learning (ML) algorithms. AMEs can be estimated for
continuous and binary independent variables / features on continuous
dependent variables / response.
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data.name |
a data frame containing the variables in the model. |
meth |
ML algorithm to be used; currently |
func |
an object of class " |
var.name |
name of independent variable / feature AME is to be estimated for. |
fromtoby |
range predicted values are to be esitmated for. Only
necessary for continuous independent variables / features. Usually
given as |
plotTree |
plot the resulting decision tree (only for |
plotPV |
plot predicted values (only for continuous independent variables / features). |
The data frame data.name
is used to train a ML model using one of
five algorithms:
method = "lm"
an ordinary linear model (yes, this is also considered a ML algorithm ;)
method = "dt"
an ordinary regression tree implemented via the
rpart
function.
method = "dtt"
two tree algorithm
method = "rf"
a random forest
method = "rftt"
random forest two tree
The formula func
is used to specify the dependent variable /
response and the independent variables / features that will be used for
learning the model.
ame
returns a list
of two (for binary independent variables /
features) or three objects (for continuous independent variables /
features):
AME estimate
predicted values (continuous only)
model information
Jonas Beste (jonas.beste@iab.de) and Arne Bethmann (bethmann@uni-mannheim.de).
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