| Title: | Solve Semi-Parametric Estimation by Implicit Profiling |
| Version: | 1.1.3 |
| Description: | Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<doi:10.48550/arXiv.2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| Suggests: | knitr, rmarkdown, numDeriv, purrr, rlang, testthat (≥ 3.0.0), BB, nleqslv, splines2 |
| VignetteBuilder: | knitr |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2021-09-01 18:53:39 UTC; su |
| Author: | Jinhua Su  [aut,
cre] |
| Maintainer: | Jinhua Su <944866518@qq.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-09-06 07:10:02 UTC |
Solve Semi-parametric estimation by implicit profiling
Description
Solve Semi-parametric estimation by implicit profiling
Usage
semislv(
theta,
lambda,
Phi_fn,
Psi_fn,
jac = list(),
intermediates = list(),
method = "implicit",
diy = FALSE,
control = list(max_iter = 100, tol = 0.001),
save = list(time = TRUE, path = FALSE),
...
)
Arguments
theta |
the initial value of parametric part |
lambda |
the initial value of non-parametric part |
Phi_fn |
the equation function highly relevant to the parametric part |
Psi_fn |
the equation function highly relevant to the non-parametric part |
jac |
a list containing some of deterivate info of Phi_der_theta_fn, Psi_der_theta_fn, Phi_der_lambda_fn, Psi_der_lambda_fn, |
intermediates |
a list containing the important variables for diy mode |
method |
"implicit" or "iterative" |
diy |
a bool value to decide to parse user designed function |
control |
a list like list(max_iter = 100, tol = 1e-3) to control the early stop |
save |
a list like list(time = FALSE, path = FALSE) to control saving setting |
... |
static parameter for Phi_fn, Psi_fn. Diy execution function. |
Value
A save space containing final iteration result and iteration path
Examples
Phi_fn <- function(theta, lambda, alpha) 2 * theta + alpha * lambda
Psi_fn <- function(theta, lambda, alpha) 2 * lambda + alpha * theta
# build quasi jacobiean by package NumDeriv
res <- semislv(1, 1, Phi_fn, Psi_fn, alpha = 1)
res <- semislv(1, 1, Phi_fn, Psi_fn, method = "iterative", alpha = 1)
# parsing all mathematical Jacobian function by user
res <- semislv(1, 1, Phi_fn, Psi_fn, jac = list(
Phi_der_theta_fn = function(theta, lambda, alpha) 2,
Phi_der_lambda_fn = function(theta, lambda, alpha) alpha,
Psi_der_theta_fn = function(theta, lambda, alpha) alpha,
Psi_der_lambda_fn = function(theta, lambda, alpha) 2
), method = "implicit", alpha = 1)
res <- semislv(1, 1, Phi_fn, Psi_fn, jac = list(
Phi_der_theta_fn = function(theta, lambda, alpha) 2,
Psi_der_lambda_fn = function(theta, lambda, alpha) 2
), method = "iterative", alpha = 1)
# parsing partial mathemetical user-provided Jacobian, the rest will be generated by the NumDeriv
res <- semislv(1, 1, Phi_fn, Psi_fn,
jac = list(Phi_der_theta_fn = function(theta, lambda, alpha) 2),
method = "implicit", alpha = 1
)
res <- semislv(1, 1, Phi_fn, Psi_fn,
jac = list(Phi_der_theta_fn = function(theta, lambda, alpha) 2),
method = "iterative", alpha = 1
)
# use some package or solve the updating totally by the user
# Cases: (1) use thirty party package (2) save the intermediates
# use diy = True, then the package will be just a wrapper for your personalise code
# diy is an advanced mode for researchers, see more examples in our vigettee documents