unweighted_gplm: Unweighted (baseline) estimator for the generalised partially...

In roseRF: ROSE Random Forests for Robust Semiparametric Efficient Estimation

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 \mathbb{E}[Y|X,Z] (regressing Y on (X,Z)).
y_on_xz_learner	a string specifying the regression method to fit the regression as given by y_on_xz_formula (e.g. randomforest, xgboost, neuralnet, gam).
y_on_xz_pars	a list containing hyperparameters for the y_on_xz_learner chosen. Default is an empty list, which performs hyperparameter tuning.
Gy_on_z_formula	a two-sided formula object describing the regression model for \mathbb{E}[g(\mathbb{E}[Y|X,Z])|Z] (regressing g(\hat{E}[Y|X,Z]) on Z).
Gy_on_z_learner	a string specifying the regression method to fit the regression as given by Gy_on_z_formula (e.g. randomforest, xgboost, neuralnet, gam).
Gy_on_z_pars	a list containing hyperparameters for the Gy_on_z_learner chosen. Default is an empty list, which performs hyperparameter tuning.
x_formula	a two-sided formula object describing the regression model for \mathbb{E}[X|Z].
x_learner	a string specifying the regression method to fit the regression of X on Z as given by x_formula (e.g. randomforest, xgboost, neuralnet, gam).
x_pars	a list containing hyperparameters for the x_learner chosen. Default is an empty list, which performs hyperparameter tuning.
link	link function (g). Options include identity, log, sqrt, logit, probit. Default is identity.
data	a data frame containing the variables for the partially linear model.
K	the number of folds used for K-fold cross-fitting. Default is 5.
S	the number of repeats to mitigate the randomness in the estimator on the sample splits used for K-fold cross-fitting. Default is 5.

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.

roseRF documentation built on Oct. 11, 2024, 9:07 a.m.