PipeOpmissMDA_PCA_MCA_FMAD_A {NADIA} | R Documentation |
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, defaultTRUE
. -
set_ncp
::integer(1)
integer >0. Number of dimensions used by algortims. Used only if optimize_ncp = Flase, default2
. -
ncp.max
::integer(1)
Number corresponding to the maximum number of components to test when optimize_ncp=TRUE, default5
. -
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
::double(1)
Value used in Regularized method, default1
. -
threshold
::double(1)
Threshold for convergence, default1e-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, defaultNULL
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpImpute
-> missMDA_MCA_PCA_FMAD_imputation_A
Methods
Public methods
Inherited methods
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
# 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))