NADIA (version 0.4.2)

PipeOpVIM_IRMI: 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, default 5.

• maxit :: integer(1)
  Maximum number of iterations, default 100

• step :: logical(1)
  Stepwise model selection is applied when the parameter is set to TRUE, default FALSE.

• 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), default FALSE.

• 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), default FALSE.

• out_fill :: character(1)
  Output log file location. If file already exists log message will be added. If NULL no log will be produced, default NULL.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpImpute -> VIM_IRMI_imputation

Methods

Public methods

• PipeOpVIM_IRMI$new()

• PipeOpVIM_IRMI$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

---

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

# \donttest{
  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))
# }