method_ps: Propensity Score Model Functions
| method_ps | R Documentation |
Propensity Score Model Functions
Description
Function to specify the propensity score (PS) model for the inverse probability weighting estimator.
This function provides basic functions logistic regression with a given link function (currently
we support logit, probit and cloglog) with additional information about the analytic variance estimator of the mean.
This is a function returns a list of functions that refer to specific estimation methods and variance estimators
when whether the IPW alone or the DR estimator is applied. The export of this function is mainly because
the functions are used in the variable selection algorithms.
Functions starting with make_log_like, make_gradient and make_hessian refer to the maximum likelihood estimation
as described in the Chen et al. (2020) paper. These functions take into account different link functions defined through
the link argument.
Functions make_link, make_link_inv, make_link_der, make_link_inv_der, make_link_inv_rev, and make_link_inv_rev_der
refer to specific link functions and are used in the estimation process.
Functions variance_covariance1 and variance_covariance2 refer to the variance estimator of the IPW estimator as
defined by Chen et al. (2020).
Functions b_vec_ipw, b_vec_dr and t_vec are specific functions defined in the Chen et al. (2020) that are used
in the variance estimator of the IPW or the DR.
Finally, var_nonprob is the non-probability component of the DR estimator as defined by Chen et al. (2020).
Usage
method_ps(link = c("logit", "probit", "cloglog"), ...)
Arguments
link |
link for the PS model |
... |
Additional, optional arguments. |
Value
A list of functions and elements for a specific link function with the following entries:
- make_log_like
log-likelihood function for a specific link function
- make_gradient
gradient of the loglik
- make_hessian
hessian of the loglik
- make_link
link function
- make_link_inv
inverse link function
- make_link_der
first derivative of the link function
- make_link_inv_der
first derivative of the the inverse link function
- make_link_inv_rev
defines 1/inv_link
- make_link_inv_rev_der
first derivative of 1/inv_link
- variance_covariance1
for the IPW estimator: variance component for the non-probability sample
- variance_covariance2
for the IPW estimator: variance component for the probability sample
- b_vec_ipw
for the IPW estimator: the
bfunction as defined in the Chen et al. (2020, sec. 3.2, eq. (9)-(10); sec 4.1)- b_vec_dr
for the DR estimator: the
bfunction as defined in the Chen et al. (2020, sec. 3.3., eq. (14); sec 4.1)- t_vec
for the DR estimator: the
bfunction as defined in the Chen et al. (2020, sec. 3.3., eq. (14); sec 4.1)- var_nonprob
for the DR estimator: non-probability component of the variance for DR estimator
- link
name of the selected link function for the PS model (character)
- model
model type (character)
Author(s)
Łukasz Chrostowski, Maciej Beręsewicz
Examples
# Printing information on the model selected
method_ps()
# extracting specific field
method_ps("cloglog")$make_gradient
