PipeOpMice_A {NADIA} | R Documentation |
PipeOpMice_A
Description
Implements mice methods as mlr3 in A approach (training imputation model on training data and used a trained model on test data).
Details
Code of used function was writen by https://github.com/prockenschaub more information aboute this aproche can be found here https://github.com/amices/mice/issues/32
Input and Output Channels
Input and output channels are inherited from PipeOpImpute
.
Parameters
The parameters include inherited from ['PipeOpImpute'], as well as:
-
id
::character(1)
Identifier of resulting object, default"imput_mice_A"
. -
m
::integer(1)
Number of datasets produced by mice, default5
. -
maxit
::integer(1)
Maximum number of iterations for mice, default5
. -
set_corr
::double(1)
Correlation or fraction of features used when optimize=FALSE. When correlation=FALSE, it represents a fraction of case to use in imputation for each variable, default0.5
. -
random.seed
::integer(1)
Random seed, default123
. -
correlation
::logical(1)
If set TRUE correlation is used, if set FALSE then fraction of case, defaultTRUE
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> mice_A_imputation
Methods
Public methods
Inherited methods
Method new()
Usage
PipeOpMice_A$new( id = "impute_mice_A", set_cor = 0.5, m = 5, maxit = 5, random.seed = 123, correlation = FALSE, methods = NULL )
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpMice_A$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
# Using debug learner for example purpose
graph <- PipeOpMice_A$new() %>>% LearnerClassifDebug$new()
graph_learner <- GraphLearner$new(graph)
# Task with NA
resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))