mlr_pipeops_fda.smooth {mlr3fda} | R Documentation |
Smoothing Functional Columns
Description
Smoothes functional data using tf::tf_smooth()
.
This preprocessing operator is similar to PipeOpFDAInterpol
, however it does not interpolate to unobserved
x-values, but rather smooths the observed values.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreprocSimple
,
as well as the following parameters:
-
method
::character(1)
One of:-
"lowess"
: locally weighted scatterplot smoothing (default) -
"rollmean"
: rolling mean -
"rollmedian"
: rolling meadian -
"savgol"
: Savitzky-Golay filtering
All methods but "lowess" ignore non-equidistant arg values.
-
-
args
:: namedlist()
List of named arguments that is passed totf_smooth()
. See the help page oftf_smooth()
for default values. -
verbose
::logical(1)
Whether to print messages during the transformation. Is initialized toFALSE
.
Super classes
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpTaskPreproc
-> mlr3pipelines::PipeOpTaskPreprocSimple
-> PipeOpFDASmooth
Methods
Public methods
Inherited methods
Method new()
Initializes a new instance of this Class.
Usage
PipeOpFDASmooth$new(id = "fda.smooth", param_vals = list())
Arguments
id
(
character(1)
)
Identifier of resulting object, default"fda.smooth"
.param_vals
(named
list
)
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Defaultlist()
.
Method clone()
The objects of this class are cloneable with this method.
Usage
PipeOpFDASmooth$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
task = tsk("fuel")
po_smooth = po("fda.smooth", method = "rollmean", args = list(k = 5))
task_smooth = po_smooth$train(list(task))[[1L]]
task_smooth
task_smooth$data(cols = c("NIR", "UVVIS"))