AverageMarginalEffects {fmeffects} | R Documentation |
R6 Class computing Average Marginal Effects (AME) based on Forward Marginal Effects (FME) for a model
Description
The AME is a simple mean FME and computed w.r.t. a feature variable and a model.
Public fields
predictor
Predictor
objectfeatures
vector of features for which AMEs should be computed
ep.method
string specifying extrapolation detection method
results
data.table with AMEs computed
computed
logical specifying if compute() has been run
Methods
Public methods
Method new()
Create a new AME object.
Usage
AverageMarginalEffects$new( model, data, target, features = NULL, ep.method = "none" )
Arguments
model
The (trained) model, with the ability to predict on new data. This must be an
Learner
(mlr3
) ortrain
(caret
) object.data
The data used for computing AMEs, must be data.frame or data.table.
target
A string specifying the model's target variable.
features
A named character vector of the names of the feature variables for which AMEs should be computed, together with the desired step sizes.
ep.method
String specifying the method used for extrapolation detection. One of
"none"
or"envelope"
. Defaults to"none"
.
Returns
A new AME
object.
Examples
# Train a model: library(mlr3verse) library(ranger) set.seed(123) data(bikes, package = "fmeffects") row.id = sample(1:nrow(bikes), 100) task = as_task_regr(x = bikes, id = "bikes", target = "count") forest = lrn("regr.ranger")$train(task) # Compute AMEs for all features: overview = AverageMarginalEffects$new( model = forest, data = bikes[row.id, ], target = "count")$compute() summary(overview) # Compute AMEs for a subset of features with non-default step.sizes: overview = AverageMarginalEffects$new(model = forest, data = bikes[row.id, ], target = "count", features = c(humidity = 0.1, weather = c("clear", "rain")))$compute() summary(overview)
Method compute()
Computes results, i.e., AMEs including the SD of FMEs, for an AME
object.
Usage
AverageMarginalEffects$compute()
Returns
An AME
object with results.
Examples
# Compute results: overview$compute()
Method clone()
The objects of this class are cloneable with this method.
Usage
AverageMarginalEffects$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
## ------------------------------------------------
## Method `AverageMarginalEffects$new`
## ------------------------------------------------
# Train a model:
library(mlr3verse)
library(ranger)
set.seed(123)
data(bikes, package = "fmeffects")
row.id = sample(1:nrow(bikes), 100)
task = as_task_regr(x = bikes, id = "bikes", target = "count")
forest = lrn("regr.ranger")$train(task)
# Compute AMEs for all features:
overview = AverageMarginalEffects$new(
model = forest,
data = bikes[row.id, ],
target = "count")$compute()
summary(overview)
# Compute AMEs for a subset of features with non-default step.sizes:
overview = AverageMarginalEffects$new(model = forest,
data = bikes[row.id, ],
target = "count",
features = c(humidity = 0.1,
weather = c("clear", "rain")))$compute()
summary(overview)
## ------------------------------------------------
## Method `AverageMarginalEffects$compute`
## ------------------------------------------------
# Compute results:
overview$compute()