, Guofu Jing [aut]| Title: | The LIC for T Distribution Regression Analysis |
|---|---|
| Description: | This comprehensive toolkit for T-distribution regression, known as the analysis of "TLIC" (T-distribution Linear regression Integrated Corrector), adopts ordinary least squares method and assumes that errors follow a T-distribution. This approach gives it an advantage when dealing with small samples or non-normal error distributions, and can provide more robust parameter estimation and hypothesis testing results.The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>. |
| Authors: | Guangbao Guo [aut, cre]
, Guofu Jing [aut] |
| Maintainer: | Guangbao Guo <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.2 |
| Built: | 2024-09-26 03:25:58 UTC |
| Source: | https://github.com/cran/TLIC |
Generate Data with T-distributed Errors
terr(n, nr, p, dist_type, ...)terr(n, nr, p, dist_type, ...)
n |
Number of observations. |
nr |
Number of observations with different error distribution. |
p |
Number of predictors. |
dist_type |
Type of distribution for the error terms. |
... |
Additional parameters for specific distributions. |
A list containing the design matrix X, the response vector Y, and the error vector e.
set.seed(12) n <- 1200 nr <- 200 p <- 5 data <- terr(n, nr, p, dist_type = "student_t") print(data$X) print(data$Y) print(data$e)set.seed(12) n <- 1200 nr <- 200 p <- 5 data <- terr(n, nr, p, dist_type = "student_t") print(data$X) print(data$Y) print(data$e)
TLIC function based on LIC with T-distributed errors
TLIC(X, Y, alpha = 0.05, K = 10, nk = NULL, dist_type = "student_t")TLIC(X, Y, alpha = 0.05, K = 10, nk = NULL, dist_type = "student_t")
X |
is a design matrix |
Y |
is a random response vector of observed values |
alpha |
is the significance level |
K |
is the number of subsets |
nk |
is the sample size of subsets |
dist_type |
Type of distribution for the error terms. |
MUopt, Bopt, MAEMUopt, MSEMUopt, opt, Yopt
set.seed(12) n <- 1200 nr <- 200 p <- 5 data <- terr(n, nr, p, dist_type = "student_t") TLIC(data$X, data$Y, alpha = 0.05, K = 10, nk = n / 10, dist_type = "student_t")set.seed(12) n <- 1200 nr <- 200 p <- 5 data <- terr(n, nr, p, dist_type = "student_t") TLIC(data$X, data$Y, alpha = 0.05, K = 10, nk = n / 10, dist_type = "student_t")