PipeOpVIM_IRMI {NADIA} | R Documentation |
PipeOpVIM_IRMI
Description
Implements IRMI methods as mlr3 pipeline, more about VIM_IRMI autotune_VIM_Irmi
.
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_VIM_IRMI"
. -
eps
::double(1)
Threshold for convergence, default5
. -
maxit
::integer(1)
Maximum number of iterations, default100
-
step
::logical(1)
Stepwise model selection is applied when the parameter is set to TRUE, defaultFALSE
. -
robust
::logical(1)
If TRUE, robust regression methods will be applied (it's impossible to set step=TRUE and robust=TRUE at the same time), defaultFALSE
. -
init.method
::character(1)
Method for initialization of missing values (kNN or median), default'kNN'
. -
force
::logical(1)
If TRUE, the algorithm tries to find a solution in any case by using different robust methods automatically (should be set FALSE for simulation), defaultFALSE
. -
out_fill
::character(1)
Output log file location. If file already exists log message will be added. If NULL no log will be produced, defaultNULL
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> VIM_IRMI_imputation
Methods
Public methods
Inherited methods
Method new()
Usage
PipeOpVIM_IRMI$new( id = "impute_VIM_IRMI_B", eps = 5, maxit = 100, step = FALSE, robust = FALSE, init.method = "kNN", force = FALSE, out_file = NULL )
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpVIM_IRMI$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
graph <- PipeOpVIM_IRMI$new() %>>% mlr3learners::LearnerClassifGlmnet$new()
graph_learner <- GraphLearner$new(graph)
# Task with NA
resample(TaskClassif$new('id',tsk('pima')$data(rows=1:100),
'diabetes'), graph_learner, rsmp("cv",folds=2))