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NADIA (version 0.4.1)

PipeOpmissMDA_MFA: PipeOpmissMDA_MFA

Description

Implements MFA methods as mlr3 pipeline, more about MFA missMDA_MFA.

Arguments

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.

  • random.seed :: integer(1)
    Integer, by default random.seed = NULL implies that missing values are initially imputed by the mean of each variable. Other values leads to a random initialization, default NULL.

  • 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

Methods

Inherited methods

Method new()

Usage

PipeOpMissMDA_MFA$new(
  id = "impute_missMDA_MFA_B",
  ncp = 2,
  random.seed = NULL,
  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$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code
# \donttest{

 # Using debug learner for example purpose

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

  # Task with NA

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

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