roseRF_pliv {roseRF} | R Documentation |
ROSE random forest estimator for the partially linear instrumental variable model
Description
ROSE random forest estimator for the partially linear instrumental variable model
Usage
roseRF_pliv(
y_formula,
y_learner,
y_pars = list(),
x_formula,
x_learner,
x_pars = list(),
IV1_formula = NA,
IV1_learner = NA,
IV1_pars = list(),
IV2_formula = NA,
IV2_learner = NA,
IV2_pars = list(),
IV3_formula = NA,
IV3_learner = NA,
IV3_pars = list(),
IV4_formula = NA,
IV4_learner = NA,
IV4_pars = list(),
IV5_formula = NA,
IV5_learner = NA,
IV5_pars = list(),
data,
K = 5,
S = 1,
max.depth = 10,
num.trees = 500,
min.node.size = max(10, ceiling(0.01 * (K - 1)/K * nrow(data))),
replace = TRUE,
sample.fraction = 0.8
)
Arguments
y_formula |
a two-sided formula object describing the regression model for |
y_learner |
a string specifying the regression method to fit the regression of |
y_pars |
a list containing hyperparameters for the |
x_formula |
a two-sided formula object describing the regression model for |
x_learner |
a string specifying the regression method to fit the regression of |
x_pars |
a list containing hyperparameters for the |
IV1_formula |
a two-sided formula object for the model |
IV1_learner |
a string specifying the regression method for |
IV1_pars |
a list containing hyperparameters for the |
IV2_formula |
a two-sided formula object for the model |
IV2_learner |
a string specifying the regression method for |
IV2_pars |
a list containing hyperparameters for the |
IV3_formula |
a two-sided formula object for the model |
IV3_learner |
a string specifying the regression method for |
IV3_pars |
a list containing hyperparameters for the |
IV4_formula |
a two-sided formula object for the model |
IV4_learner |
a string specifying the regression method for |
IV4_pars |
a list containing hyperparameters for the |
IV5_formula |
a two-sided formula object for the model |
IV5_learner |
a string specifying the regression method for |
IV5_pars |
a list containing hyperparameters for the |
data |
a data frame containing the variables for the partially linear model. |
K |
the number of folds used for |
S |
the number of repeats to mitigate the randomness in the estimator on the sample splits used for |
max.depth |
Maximum depth parameter used for ROSE random forests. Default is 5. |
num.trees |
Number of trees used for a single ROSE random forest. Default is 50. |
min.node.size |
Minimum node size of a leaf in each tree. Default is |
replace |
Whether sampling for a single random tree are performed with (bootstrap) or without replacement. Default is |
sample.fraction |
Proportion of data used for each random tree. Default is 0.8. |
Value
A list containing:
theta
The estimator of
\theta_0
.stderror
Huber robust estimate of the standard error of the
\theta_0
-estimator.coefficients
Table of
\theta_0
coefficient estimator, standard error, z-value and p-value.