mlr_pipeops_fda.smooth {mlr3fda} | R Documentation |
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.
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
:: named list()
List of named arguments that is passed to tf_smooth()
. See the help page of tf_smooth()
for
default values.
verbose
:: logical(1)
Whether to print messages during the transformation.
Is initialized to FALSE
.
mlr3pipelines::PipeOp
-> mlr3pipelines::PipeOpTaskPreproc
-> mlr3pipelines::PipeOpTaskPreprocSimple
-> PipeOpFDASmooth
new()
Initializes a new instance of this Class.
PipeOpFDASmooth$new(id = "fda.smooth", param_vals = list())
id
(character(1)
)
Identifier of resulting object, default "fda.smooth"
.
param_vals
(named list
)
List of hyperparameter settings, overwriting the hyperparameter settings that would
otherwise be set during construction. Default list()
.
clone()
The objects of this class are cloneable with this method.
PipeOpFDASmooth$clone(deep = FALSE)
deep
Whether to make a deep clone.
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"))