NADIA (version 0.4.2)

PipeOpmissMDA_PCA_MCA_FMAD_A: PipeOpmissMDA_PCA_MCA_FMAD_A

Description

Implements PCA, MCA, FMAD methods as mlr3 pipeline in approach A, more about methods missMDA_FMAD_MCA_PCA 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_MCA_PCA_FMAD".

• optimize_ncp :: logical(1)
  If TRUE, parameter number of dimensions, used to predict the missing values, will be optimized. If FALSE, by default ncp=2 is used, default TRUE.

• set_ncp :: integer(1)
  integer >0. Number of dimensions used by algortims. Used only if optimize_ncp = Flase, default 2.

• ncp.max :: integer(1)
  Number corresponding to the maximum number of components to test when optimize_ncp=TRUE, default 5.

• 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 :: double(1)
  Value used in Regularized method, default 1.

• threshold :: double(1)
  Threshold for convergence, default 1e-6.

• 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_MCA_PCA_FMAD_imputation_A

Methods

Public methods

• PipeOpMissMDA_PCA_MCA_FMAD_A$new()

• PipeOpMissMDA_PCA_MCA_FMAD_A$clone()

Inherited methods

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

---

Method new()

Usage

PipeOpMissMDA_PCA_MCA_FMAD_A$new(
  id = "impute_missMDA_MCA_PCA_FMAD_A",
  optimize_ncp = TRUE,
  set_ncp = 2,
  ncp.max = 5,
  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_PCA_MCA_FMAD_A$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{

 # Using debug learner for example purpose


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

  # Task with NA
  set.seed(1)
  resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))
# }