PipeOpmissMDA_MFA {NADIA} | R Documentation |
PipeOpmissMDA_MFA
Description
Implements MFA methods as mlr3 pipeline, more about MFA missMDA_MFA
.
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, default2
. -
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, defaultNULL
. -
maxiter
::integer(1)
Maximal number of iteration in algorithm, default998
. -
coeff.ridge
::integer(1)
Value used in Regularized method, default1
. -
threshold
::double(1)
Threshold for convergence, default1e-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, defaultNULL
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> missMDA_MFAimputation
Methods
Public 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
# 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))