DoubleML: Abstract class DoubleML
In DoubleML: Double Machine Learning in R
DoubleML R Documentation
Abstract class DoubleML
Description
Abstract base class that can't be initialized.
Format
R6::R6Class object.
Active bindings
all_coef(matrix())
Estimates of the causal parameter(s) for the n_rep different sample
splits after calling fit().
all_dml1_coef(array())
Estimates of the causal parameter(s) for the n_rep different sample
splits after calling fit() with dml_procedure = "dml1".
all_se(matrix())
Standard errors of the causal parameter(s) for the n_rep different
sample splits after calling fit().
apply_cross_fitting(logical(1))
Indicates whether cross-fitting should be applied. Default is TRUE.
boot_coef(matrix())
Bootstrapped coefficients for the causal parameter(s) after calling
fit() and bootstrap().
boot_t_stat(matrix())
Bootstrapped t-statistics for the causal parameter(s) after calling
fit() and bootstrap().
coef(numeric())
Estimates for the causal parameter(s) after calling fit().
data(data.table)
Data object.
dml_procedure(character(1))
A character() ("dml1" or "dml2") specifying the double machine
learning algorithm. Default is "dml2".
draw_sample_splitting(logical(1))
Indicates whether the sample splitting should be drawn during
initialization of the object. Default is TRUE.
learner(named list())
The machine learners for the nuisance functions.
n_folds(integer(1))
Number of folds. Default is 5.
n_rep(integer(1))
Number of repetitions for the sample splitting. Default is 1.
params(named list())
The hyperparameters of the learners.
psi(array())
Value of the score function
\psi(W;\theta, \eta)=\psi_a(W;\eta) \theta + \psi_b (W; \eta)
after calling fit().
psi_a(array())
Value of the score function component \psi_a(W;\eta) after
calling fit().
psi_b(array())
Value of the score function component \psi_b(W;\eta) after
calling fit().
predictions(array())
Predictions of the nuisance models after calling
fit(store_predictions=TRUE).
models(array())
The fitted nuisance models after calling
fit(store_models=TRUE).
pval(numeric())
p-values for the causal parameter(s) after calling fit().
score(character(1), function())
A character(1) or function() specifying the score function.
se(numeric())
Standard errors for the causal parameter(s) after calling fit().
smpls(list())
The partition used for cross-fitting.
smpls_cluster(list())
The partition of clusters used for cross-fitting.
t_stat(numeric())
t-statistics for the causal parameter(s) after calling fit().
tuning_res(named list())
Results from hyperparameter tuning.
Methods
Public methods
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Method new()
DoubleML is an abstract class that can't be initialized.
Usage
DoubleML$new()
Method print()
Print DoubleML objects.
Usage
DoubleML$print()
Method fit()
Estimate DoubleML models.
Usage
DoubleML$fit(store_predictions = FALSE, store_models = FALSE)
Arguments
store_predictions(logical(1))
Indicates whether the predictions for the nuisance functions should be
stored in field predictions. Default is FALSE.
store_models(logical(1))
Indicates whether the fitted models for the nuisance functions should be
stored in field models if you want to analyze the models or extract
information like variable importance. Default is FALSE.
Returns
self
Method bootstrap()
Multiplier bootstrap for DoubleML models.
Usage
DoubleML$bootstrap(method = "normal", n_rep_boot = 500)
Arguments
method(character(1))
A character(1) ("Bayes", "normal" or "wild") specifying the
multiplier bootstrap method.
n_rep_boot(integer(1))
The number of bootstrap replications.
Returns
self
Method split_samples()
Draw sample splitting for DoubleML models.
The samples are drawn according to the attributes n_folds, n_rep
and apply_cross_fitting.
Usage
DoubleML$split_samples()
Returns
self
Method set_sample_splitting()
Set the sample splitting for DoubleML models.
The attributes n_folds and n_rep are derived from the provided
partition.
Usage
DoubleML$set_sample_splitting(smpls)
Arguments
smpls(list())
A nested list(). The outer lists needs to provide an entry per
repeated sample splitting (length of the list is set as n_rep).
The inner list is a named list() with names train_ids and test_ids.
The entries in train_ids and test_ids must be partitions per fold
(length of train_ids and test_ids is set as n_folds).
Returns
self
Examples
library(DoubleML)
library(mlr3)
set.seed(2)
obj_dml_data = make_plr_CCDDHNR2018(n_obs=10)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data,
lrn("regr.rpart"), lrn("regr.rpart"))
# simple sample splitting with two folds and without cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5)),
test_ids = list(c(6, 7, 8, 9, 10))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and cross-fitting but no repeated cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and repeated cross-fitting with n_rep = 2
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))),
list(train_ids = list(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10)),
test_ids = list(c(2, 4, 6, 8, 10), c(1, 3, 5, 7, 9))))
dml_plr_obj$set_sample_splitting(smpls)
Method tune()
Hyperparameter-tuning for DoubleML models.
The hyperparameter-tuning is performed using the tuning methods provided
in the mlr3tuning package. For more
information on tuning in mlr3, we refer to
the section on parameter tuning in the
mlr3 book.
Usage
DoubleML$tune(
param_set,
tune_settings = list(n_folds_tune = 5, rsmp_tune = mlr3::rsmp("cv", folds = 5), measure
= NULL, terminator = mlr3tuning::trm("evals", n_evals = 20), algorithm =
mlr3tuning::tnr("grid_search"), resolution = 5),
tune_on_folds = FALSE
)
Arguments
param_set(named list())
A named list with a parameter grid for each nuisance model/learner
(see method learner_names()). The parameter grid must be an object of
class ParamSet.
tune_settings(named list())
A named list() with arguments passed to the hyperparameter-tuning with
mlr3tuning to set up
TuningInstance objects.
tune_settings has entries
-
terminator (Terminator)
A Terminator object. Specification of terminator
is required to perform tuning.
-
algorithm (Tuner or character(1))
A Tuner object (recommended) or key passed to the
respective dictionary to specify the tuning algorithm used in
tnr(). algorithm is passed as an argument to
tnr(). If algorithm is not specified by the users,
default is set to "grid_search". If set to "grid_search", then
additional argument "resolution" is required.
-
rsmp_tune (Resampling or character(1))
A Resampling object (recommended) or option passed
to rsmp() to initialize a
Resampling for parameter tuning in mlr3.
If not specified by the user, default is set to "cv"
(cross-validation).
-
n_folds_tune (integer(1), optional)
If rsmp_tune = "cv", number of folds used for cross-validation.
If not specified by the user, default is set to 5.
-
measure (NULL, named list(), optional)
Named list containing the measures used for parameter tuning. Entries in
list must either be Measure objects or keys to be
passed to passed to msr(). The names of the entries must
match the learner names (see method learner_names()). If set to NULL,
default measures are used, i.e., "regr.mse" for continuous outcome
variables and "classif.ce" for binary outcomes.
-
resolution (character(1))
The key passed to the respective
dictionary to specify the tuning algorithm used in
tnr(). resolution is passed as an argument to
tnr().
tune_on_folds(logical(1))
Indicates whether the tuning should be done fold-specific or globally.
Default is FALSE.
Returns
self
Method summary()
Summary for DoubleML models after calling fit().
Usage
DoubleML$summary(digits = max(3L, getOption("digits") - 3L))
Arguments
digits(integer(1))
The number of significant digits to use when printing.
Method confint()
Confidence intervals for DoubleML models.
Usage
DoubleML$confint(parm, joint = FALSE, level = 0.95)
Arguments
parm(numeric() or character())
A specification of which parameters are to be given confidence intervals
among the variables for which inference was done, either a vector of
numbers or a vector of names. If missing, all parameters are considered
(default).
joint(logical(1))
Indicates whether joint confidence intervals are computed.
Default is FALSE.
level(numeric(1))
The confidence level. Default is 0.95.
Returns
A matrix() with the confidence interval(s).
Method learner_names()
Returns the names of the learners.
Usage
DoubleML$learner_names()
Returns
character() with names of learners.
Method params_names()
Returns the names of the nuisance models with hyperparameters.
Usage
DoubleML$params_names()
Returns
character() with names of nuisance models with hyperparameters.
Method set_ml_nuisance_params()
Set hyperparameters for the nuisance models of DoubleML models.
Note that in the current implementation, either all parameters have to
be set globally or all parameters have to be provided fold-specific.
Usage
DoubleML$set_ml_nuisance_params(
learner = NULL,
treat_var = NULL,
params,
set_fold_specific = FALSE
)
Arguments
learner(character(1))
The nuisance model/learner (see method params_names).
treat_var(character(1))
The treatment varaible (hyperparameters can be set treatment-variable
specific).
params(named list())
A named list() with estimator parameters. Parameters are used for all
folds by default. Alternatively, parameters can be passed in a
fold-specific way if option fold_specificis TRUE. In this case, the
outer list needs to be of length n_rep and the inner list of length
n_folds.
set_fold_specific(logical(1))
Indicates if the parameters passed in params should be passed in
fold-specific way. Default is FALSE. If TRUE, the outer list needs
to be of length n_rep and the inner list of length n_folds.
Note that in the current implementation, either all parameters have to
be set globally or all parameters have to be provided fold-specific.
Returns
self
Method p_adjust()
Multiple testing adjustment for DoubleML models.
Usage
DoubleML$p_adjust(method = "romano-wolf", return_matrix = TRUE)
Arguments
method(character(1))
A character(1)("romano-wolf", "bonferroni", "holm", etc)
specifying the adjustment method. In addition to "romano-wolf",
all methods implemented in p.adjust() can be
applied. Default is "romano-wolf".
return_matrix(logical(1))
Indicates if the output is returned as a matrix with corresponding
coefficient names.
Returns
numeric() with adjusted p-values. If return_matrix = TRUE,
a matrix() with adjusted p_values.
Method get_params()
Get hyperparameters for the nuisance model of DoubleML models.
Usage
DoubleML$get_params(learner)
Arguments
learner(character(1))
The nuisance model/learner (see method params_names())
Returns
named list()with paramers for the nuisance model/learner.
Method clone()
The objects of this class are cloneable with this method.
Usage
DoubleML$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other DoubleML:
DoubleMLIIVM,
DoubleMLIRM,
DoubleMLPLIV,
DoubleMLPLR
Examples
## ------------------------------------------------
## Method `DoubleML$set_sample_splitting`
## ------------------------------------------------
library(DoubleML)
library(mlr3)
set.seed(2)
obj_dml_data = make_plr_CCDDHNR2018(n_obs=10)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data,
lrn("regr.rpart"), lrn("regr.rpart"))
# simple sample splitting with two folds and without cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5)),
test_ids = list(c(6, 7, 8, 9, 10))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and cross-fitting but no repeated cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and repeated cross-fitting with n_rep = 2
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))),
list(train_ids = list(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10)),
test_ids = list(c(2, 4, 6, 8, 10), c(1, 3, 5, 7, 9))))
dml_plr_obj$set_sample_splitting(smpls)
DoubleML documentation built on April 1, 2023, 12:16 a.m.
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| DoubleML | R Documentation |
Abstract class DoubleML
Description
Abstract base class that can't be initialized.
Format
R6::R6Class object.
Active bindings
all_coef(
matrix())
Estimates of the causal parameter(s) for then_repdifferent sample splits after callingfit().all_dml1_coef(
array())
Estimates of the causal parameter(s) for then_repdifferent sample splits after callingfit()withdml_procedure = "dml1".all_se(
matrix())
Standard errors of the causal parameter(s) for then_repdifferent sample splits after callingfit().apply_cross_fitting(
logical(1))
Indicates whether cross-fitting should be applied. Default isTRUE.boot_coef(
matrix())
Bootstrapped coefficients for the causal parameter(s) after callingfit()andbootstrap().boot_t_stat(
matrix())
Bootstrapped t-statistics for the causal parameter(s) after callingfit()andbootstrap().coef(
numeric())
Estimates for the causal parameter(s) after callingfit().data(
data.table)
Data object.dml_procedure(
character(1))
Acharacter()("dml1"or"dml2") specifying the double machine learning algorithm. Default is"dml2".draw_sample_splitting(
logical(1))
Indicates whether the sample splitting should be drawn during initialization of the object. Default isTRUE.learner(named
list())
The machine learners for the nuisance functions.n_folds(
integer(1))
Number of folds. Default is5.n_rep(
integer(1))
Number of repetitions for the sample splitting. Default is1.params(named
list())
The hyperparameters of the learners.psi(
array())
Value of the score function\psi(W;\theta, \eta)=\psi_a(W;\eta) \theta + \psi_b (W; \eta)after callingfit().psi_a(
array())
Value of the score function component\psi_a(W;\eta)after callingfit().psi_b(
array())
Value of the score function component\psi_b(W;\eta)after callingfit().predictions(
array())
Predictions of the nuisance models after callingfit(store_predictions=TRUE).models(
array())
The fitted nuisance models after callingfit(store_models=TRUE).pval(
numeric())
p-values for the causal parameter(s) after callingfit().score(
character(1),function())
Acharacter(1)orfunction()specifying the score function.se(
numeric())
Standard errors for the causal parameter(s) after callingfit().smpls(
list())
The partition used for cross-fitting.smpls_cluster(
list())
The partition of clusters used for cross-fitting.t_stat(
numeric())
t-statistics for the causal parameter(s) after callingfit().tuning_res(named
list())
Results from hyperparameter tuning.
Methods
Public methods
Method new()
DoubleML is an abstract class that can't be initialized.
Usage
DoubleML$new()
Method print()
Print DoubleML objects.
Usage
DoubleML$print()
Method fit()
Estimate DoubleML models.
Usage
DoubleML$fit(store_predictions = FALSE, store_models = FALSE)
Arguments
store_predictions(
logical(1))
Indicates whether the predictions for the nuisance functions should be stored in fieldpredictions. Default isFALSE.store_models(
logical(1))
Indicates whether the fitted models for the nuisance functions should be stored in fieldmodelsif you want to analyze the models or extract information like variable importance. Default isFALSE.
Returns
self
Method bootstrap()
Multiplier bootstrap for DoubleML models.
Usage
DoubleML$bootstrap(method = "normal", n_rep_boot = 500)
Arguments
method(
character(1))
Acharacter(1)("Bayes","normal"or"wild") specifying the multiplier bootstrap method.n_rep_boot(
integer(1))
The number of bootstrap replications.
Returns
self
Method split_samples()
Draw sample splitting for DoubleML models.
The samples are drawn according to the attributes n_folds, n_rep
and apply_cross_fitting.
Usage
DoubleML$split_samples()
Returns
self
Method set_sample_splitting()
Set the sample splitting for DoubleML models.
The attributes n_folds and n_rep are derived from the provided
partition.
Usage
DoubleML$set_sample_splitting(smpls)
Arguments
smpls(
list())
A nestedlist(). The outer lists needs to provide an entry per repeated sample splitting (length of the list is set asn_rep). The inner list is a namedlist()with namestrain_idsandtest_ids. The entries intrain_idsandtest_idsmust be partitions per fold (length oftrain_idsandtest_idsis set asn_folds).
Returns
self
Examples
library(DoubleML)
library(mlr3)
set.seed(2)
obj_dml_data = make_plr_CCDDHNR2018(n_obs=10)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data,
lrn("regr.rpart"), lrn("regr.rpart"))
# simple sample splitting with two folds and without cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5)),
test_ids = list(c(6, 7, 8, 9, 10))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and cross-fitting but no repeated cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and repeated cross-fitting with n_rep = 2
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))),
list(train_ids = list(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10)),
test_ids = list(c(2, 4, 6, 8, 10), c(1, 3, 5, 7, 9))))
dml_plr_obj$set_sample_splitting(smpls)
Method tune()
Hyperparameter-tuning for DoubleML models.
The hyperparameter-tuning is performed using the tuning methods provided in the mlr3tuning package. For more information on tuning in mlr3, we refer to the section on parameter tuning in the mlr3 book.
Usage
DoubleML$tune(
param_set,
tune_settings = list(n_folds_tune = 5, rsmp_tune = mlr3::rsmp("cv", folds = 5), measure
= NULL, terminator = mlr3tuning::trm("evals", n_evals = 20), algorithm =
mlr3tuning::tnr("grid_search"), resolution = 5),
tune_on_folds = FALSE
)Arguments
param_set(named
list())
A namedlistwith a parameter grid for each nuisance model/learner (see methodlearner_names()). The parameter grid must be an object of class ParamSet.tune_settings(named
list())
A namedlist()with arguments passed to the hyperparameter-tuning with mlr3tuning to set up TuningInstance objects.tune_settingshas entries-
terminator(Terminator)
A Terminator object. Specification ofterminatoris required to perform tuning. -
algorithm(Tuner orcharacter(1))
A Tuner object (recommended) or key passed to the respective dictionary to specify the tuning algorithm used in tnr().algorithmis passed as an argument to tnr(). Ifalgorithmis not specified by the users, default is set to"grid_search". If set to"grid_search", then additional argument"resolution"is required. -
rsmp_tune(Resampling orcharacter(1))
A Resampling object (recommended) or option passed to rsmp() to initialize a Resampling for parameter tuning inmlr3. If not specified by the user, default is set to"cv"(cross-validation). -
n_folds_tune(integer(1), optional)
Ifrsmp_tune = "cv", number of folds used for cross-validation. If not specified by the user, default is set to5. -
measure(NULL, namedlist(), optional)
Named list containing the measures used for parameter tuning. Entries in list must either be Measure objects or keys to be passed to passed to msr(). The names of the entries must match the learner names (see methodlearner_names()). If set toNULL, default measures are used, i.e.,"regr.mse"for continuous outcome variables and"classif.ce"for binary outcomes. -
resolution(character(1))
The key passed to the respective dictionary to specify the tuning algorithm used in tnr().resolutionis passed as an argument to tnr().
-
tune_on_folds(
logical(1))
Indicates whether the tuning should be done fold-specific or globally. Default isFALSE.
Returns
self
Method summary()
Summary for DoubleML models after calling fit().
Usage
DoubleML$summary(digits = max(3L, getOption("digits") - 3L))Arguments
digits(
integer(1))
The number of significant digits to use when printing.
Method confint()
Confidence intervals for DoubleML models.
Usage
DoubleML$confint(parm, joint = FALSE, level = 0.95)
Arguments
parm(
numeric()orcharacter())
A specification of which parameters are to be given confidence intervals among the variables for which inference was done, either a vector of numbers or a vector of names. If missing, all parameters are considered (default).joint(
logical(1))
Indicates whether joint confidence intervals are computed. Default isFALSE.level(
numeric(1))
The confidence level. Default is0.95.
Returns
A matrix() with the confidence interval(s).
Method learner_names()
Returns the names of the learners.
Usage
DoubleML$learner_names()
Returns
character() with names of learners.
Method params_names()
Returns the names of the nuisance models with hyperparameters.
Usage
DoubleML$params_names()
Returns
character() with names of nuisance models with hyperparameters.
Method set_ml_nuisance_params()
Set hyperparameters for the nuisance models of DoubleML models.
Note that in the current implementation, either all parameters have to be set globally or all parameters have to be provided fold-specific.
Usage
DoubleML$set_ml_nuisance_params( learner = NULL, treat_var = NULL, params, set_fold_specific = FALSE )
Arguments
learner(
character(1))
The nuisance model/learner (see methodparams_names).treat_var(
character(1))
The treatment varaible (hyperparameters can be set treatment-variable specific).params(named
list())
A namedlist()with estimator parameters. Parameters are used for all folds by default. Alternatively, parameters can be passed in a fold-specific way if optionfold_specificisTRUE. In this case, the outer list needs to be of lengthn_repand the inner list of lengthn_folds.set_fold_specific(
logical(1))
Indicates if the parameters passed inparamsshould be passed in fold-specific way. Default isFALSE. IfTRUE, the outer list needs to be of lengthn_repand the inner list of lengthn_folds. Note that in the current implementation, either all parameters have to be set globally or all parameters have to be provided fold-specific.
Returns
self
Method p_adjust()
Multiple testing adjustment for DoubleML models.
Usage
DoubleML$p_adjust(method = "romano-wolf", return_matrix = TRUE)
Arguments
method(
character(1))
Acharacter(1)("romano-wolf","bonferroni","holm", etc) specifying the adjustment method. In addition to"romano-wolf", all methods implemented in p.adjust() can be applied. Default is"romano-wolf".return_matrix(
logical(1))
Indicates if the output is returned as a matrix with corresponding coefficient names.
Returns
numeric() with adjusted p-values. If return_matrix = TRUE,
a matrix() with adjusted p_values.
Method get_params()
Get hyperparameters for the nuisance model of DoubleML models.
Usage
DoubleML$get_params(learner)
Arguments
learner(
character(1))
The nuisance model/learner (see methodparams_names())
Returns
named list()with paramers for the nuisance model/learner.
Method clone()
The objects of this class are cloneable with this method.
Usage
DoubleML$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other DoubleML:
DoubleMLIIVM,
DoubleMLIRM,
DoubleMLPLIV,
DoubleMLPLR
Examples
## ------------------------------------------------
## Method `DoubleML$set_sample_splitting`
## ------------------------------------------------
library(DoubleML)
library(mlr3)
set.seed(2)
obj_dml_data = make_plr_CCDDHNR2018(n_obs=10)
dml_plr_obj = DoubleMLPLR$new(obj_dml_data,
lrn("regr.rpart"), lrn("regr.rpart"))
# simple sample splitting with two folds and without cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5)),
test_ids = list(c(6, 7, 8, 9, 10))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and cross-fitting but no repeated cross-fitting
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))))
dml_plr_obj$set_sample_splitting(smpls)
# sample splitting with two folds and repeated cross-fitting with n_rep = 2
smpls = list(list(train_ids = list(c(1, 2, 3, 4, 5), c(6, 7, 8, 9, 10)),
test_ids = list(c(6, 7, 8, 9, 10), c(1, 2, 3, 4, 5))),
list(train_ids = list(c(1, 3, 5, 7, 9), c(2, 4, 6, 8, 10)),
test_ids = list(c(2, 4, 6, 8, 10), c(1, 3, 5, 7, 9))))
dml_plr_obj$set_sample_splitting(smpls)
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