PipeOpMice {NADIA} | R Documentation |
PipeOpMice
Description
Implements mice methods as mlr3 pipeline more about mice autotune_mice
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_mice"
. -
m
::integer(1)
Number of datasets produced by mice, default5
. -
maxit
::integer(1)
Maximum number of iterations for mice, default5
. -
set_corr
::double(1)
Correlation or fraction of features used when optimize=FALSE. When correlation=FALSE, it represents a fraction of case to use in imputation for each variable, default0.5
. -
set_method
::character(1)
Method used if optimize=FALSE. If NULL default method is used (more in methods_random section), default'pmm'
. -
low_corr
::double(1)
Double between 0-1. Lower boundary of correlation used in inner optimization (used only when optimize=TRUE), default0
. -
up_corr
::double(1)
Double between 0-1. Upper boundary of correlation used in inner optimization (used only when optimize=TRUE). Both of these parameters work the same for a fraction of case if correlation=FALSE,default1
. -
methods_random
::character(1)
set of methods to chose. Avalible methods "pmm", "midastouch", "sample", "cart", "rf" Default 'pmm'. If seted on NULL this methods are used predictive mean matching (numeric data) logreg, logistic regression imputation (binary data, factor with 2 levels) polyreg, polytomous regression imputation for unordered categorical data (factor > 2 levels) polr, proportional odds model for (ordered, > 2 levels). -
iter
::integer(1)
Number of iteration for random search, default5
. -
random.seed
::integer(1)
Random seed, default123
. -
optimize
::logical(1)
If set TRUE, function will optimize parameters of imputation automatically. If parameters will be tuned by other method, should be set to FALSE, defaultFALSE
. -
correlation
::logical(1)
If set TRUE correlation is used, if set FALSE then fraction of case, defaultTRUE
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> mice_imputation
Methods
Public methods
Inherited methods
Method new()
Usage
PipeOpMice$new( id = "impute_mice_B", m = 5, maxit = 5, set_cor = 0.5, set_method = "pmm", low_corr = 0, up_corr = 1, methods_random = c("pmm"), iter = 5, random.seed = 123, optimize = FALSE, correlation = FALSE, out_file = NULL )
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpMice$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
# Using debug learner for example purpose
graph <- PipeOpMice$new() %>>% LearnerClassifDebug$new()
graph_learner <- GraphLearner$new(graph)
# Task with NA
resample(tsk("pima"), graph_learner, rsmp("cv", folds = 3))