unweighted_gplm {roseRF} | R Documentation |
Unweighted (baseline) estimator for the generalised partially linear model
Description
Estimates the parameter of interest \theta_0
in the generalised partially linear regression model
g(\mathbb{E}[Y|X,Z]) = X\theta_0 + f_0(Z),
as in roseRF_gplm
but without
any weights i.e. J=1
, M_1(X)=X
and w_1\equiv 1
.
Usage
unweighted_gplm(
y_on_xz_formula,
y_on_xz_learner,
y_on_xz_pars = list(),
Gy_on_z_formula,
Gy_on_z_learner,
Gy_on_z_pars = list(),
x_formula,
x_learner,
x_pars = list(),
link = "identity",
data,
K = 5,
S = 1
)
Arguments
y_on_xz_formula |
a two-sided formula object describing the regression model for |
y_on_xz_learner |
a string specifying the regression method to fit the regression as given by |
y_on_xz_pars |
a list containing hyperparameters for the |
Gy_on_z_formula |
a two-sided formula object describing the regression model for |
Gy_on_z_learner |
a string specifying the regression method to fit the regression as given by |
Gy_on_z_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 |
link |
link function ( |
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 |
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.