PipeOpAmelia: PipeOpAmelia

In NADIA: NA Data Imputation Algorithms

PipeOpAmelia

Description

Implements EMB methods as mlr3 pipeline more about Amelia autotune_Amelia or https://cran.r-project.org/package=Amelia

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_Amelia".

• m :: integer(1)
  Number of datasets generated by Amelia, default 3.

• polytime :: integer(1)
  Integer between 0 and 3 indicating what power of polynomial should be included in the imputation model to account for the effects of time. A setting of 0 would indicate constant levels, 1 would indicate linear time effects, 2 would indicate squared effects, and 3 would indicate cubic time effects, default NULL.

• splinetime :: integer(1)
  Integer value of 0 or greater to control cubic smoothing splines of time. Values between 0 and 3 create a simple polynomial of time (identical to the polytime argument). Values k greater than 3 create a spline with an additional k-3 knotpoints, default NULL.

• intercs :: logical(1)
  Variable indicating if the time effects of polytime should vary across the cross-section, default FALSE.

• empir :: double(1)
  Number indicating level of the empirical (or ridge) prior. This prior shrinks the covariances of the data, but keeps the means and variances the same for problems of high missingness, small N's or large correlations among the variables. Should be kept small, perhaps 0.5 to 1 percent of the rows of the data; a reasonable upper bound is around 10 percent of the rows of the data. If empir is not set, empir=nrow(df)*0.015, default NULL.

• parallel :: double(1)
  If true parallel calculation is used, default TRUE.

• 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 -> Amelia_imputation

Methods

Public methods

• PipeOpAmelia$new()

• PipeOpAmelia$clone()

Inherited methods

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

---

Method new()

Usage

PipeOpAmelia$new(
  id = "impute_Amelia_B",
  polytime = NULL,
  splinetime = NULL,
  intercs = FALSE,
  empir = NULL,
  m = 3,
  parallel = TRUE,
  out_file = NULL
)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpAmelia$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Using debug learner for example purpose

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

  graph_learner$param_set$values$impute_Amelia_B.parallel <- FALSE


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

NADIA documentation built on Oct. 3, 2022, 1:05 a.m.