PipeOpmissMDA_MFA_A {NADIA}

PipeOpmissMDA_MFA_A

Description

Implements MFA methods as mlr3 pipeline in A approche , more about MFA missMDA_MFA and missMDA.reuse

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_missMDA_MFA".

• ncp :: integer(1)
  Number of dimensions used by algorithm, default 2.

• maxiter :: integer(1)
  Maximal number of iteration in algorithm, default 998.

• coeff.ridge :: integer(1)
  Value used in Regularized method, default 1.

• threshold :: double(1)
  Threshold for convergence, default 1e-06.

• method :: character(1)
  Method used in imputation algorithm, default 'Regularized'.

• 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 -> missMDA_MFAimputation_A

Methods

Public methods

• PipeOpMissMDA_MFA_A$new()

• PipeOpMissMDA_MFA_A$clone()

Inherited methods

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

Method new()

Usage

PipeOpMissMDA_MFA_A$new(
  id = "impute_missMDA_MFA_A",
  ncp = 2,
  maxiter = 998,
  coeff.ridge = 1,
  threshold = 1e-06,
  method = "Regularized",
  out_file = NULL
)

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpMissMDA_MFA_A$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

 # Using debug learner for example purpose

  graph <- PipeOpMissMDA_MFA_A$new() %>>% LearnerClassifDebug$new()
  graph_learner <- GraphLearner$new(graph)

  # Task with NA

  resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))