文件列表:

.Rbuildignore (49, 2023-10-24)
DESCRIPTION (1076, 2023-10-24)
LICENSE.md (34904, 2023-10-24)
NAMESPACE (508, 2023-10-24)
NEWS.md (82, 2023-10-24)
R/ (0, 2023-10-24)
R/softtbart1.R (24365, 2023-10-24)
R/softtbart1np.R (50772, 2023-10-24)
R/softtbart2.R (57424, 2023-10-24)
R/softtbart2np.R (119987, 2023-10-24)
R/tbart1.R (25038, 2023-10-24)
R/tbart1np.R (48639, 2023-10-24)
R/tbart2c.R (54542, 2023-10-24)
R/tbart2np.R (115110, 2023-10-24)
TobitBART.Rproj (386, 2023-10-24)
man/ (0, 2023-10-24)
man/softtbart1.Rd (6837, 2023-10-24)
man/softtbart1np.Rd (11836, 2023-10-24)
man/softtbart2.Rd (18047, 2023-10-24)
man/softtbart2np.Rd (18063, 2023-10-24)
man/tbart1.Rd (10047, 2023-10-24)
man/tbart1np.Rd (11770, 2023-10-24)
man/tbart2c.Rd (18203, 2023-10-24)
man/tbart2np.Rd (18077, 2023-10-24)

# TobitBART The goal of TobitBART is to provide implementations of type 1 and type 2 Tobit models with Bayesian Additive Regression Trees (Chipman et al. 2010) instead of linear combinations of covariates. Sums-of-trees are sampled using the ``dbarts`` package. The Type 1 Tobit implementaiton is based on Chib (1992). The Type 2 Tobit implementaiton is based on Omori (2007), van Hasselt (2011), and Ding (2014). The `tbart1` function runs Type 1 TOBART. The `tbart1np` function runs Type 1 TOBART with a Dirichlet Process mixture distribution for the error (George et al. 2019). The `softtbart1` function runs Type 1 TOBART with soft trees and a hyperprior on splitting variables for sparse data generating processes (Linero and Yang 2018). The `softtbart1np` function runs Type 1 TOBART with with soft trees, a hyperprior on splitting variables for sparse data generating processes (Linero and Yang 2018), and a Dirichlet Process mixture distribution for the error (George et al. 2019). The `tbart2c` function runs Type 2 TOBART with bivariate normal errors in the selection and outcome equations. [Not tested yet] The `tbart2np` function runs nonparametric Type 2 TOBART. The errors in the selection and outcome equations are jointly distributed as a Dirichlet Process mixture of bivariate normal distributions. [Not tested yet] The `softtbart2` function runs Type 2 TOBART with bivariate normal errors in the selection and outcome equations, soft trees, and a hyperprior on splitting variables for sparse data generating processes (Linero and Yang 2018) . [Not tested yet] The `softtbart2np` function runs nonparametric Type 2 TOBART with soft trees, and a hyperprior on splitting variables for sparse data generating processes (Linero and Yang 2018). The errors in the selection and outcome equations are jointly distributed as a Dirichlet Process mixture of bivariate normal distributions. [Not tested yet] Chib, S. (1992). Bayes inference in the Tobit censored regression model. Journal of Econometrics, 51(1-2), 79-99. Ding, P. (2014). Bayesian robust inference of sample selection using selection-t models. Journal of Multivariate Analysis, 124, 451-464. George, E., Laud, P., Logan, B., McCulloch, R., & Sparapani, R. (2019). Fully nonparametric Bayesian additive regression trees. In Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B (Vol. 40, pp. 89-110). Emerald Publishing Limited. Linero, A. R., & Yang, Y. (2018). Bayesian regression tree ensembles that adapt to smoothness and sparsity. Journal of the Royal Statistical Society Series B: Statistical Methodology, 80(5), 1087-1110. Omori, Y. (2007). Efficient Gibbs sampler for Bayesian analysis of a sample selection model. Statistics & probability letters, 77(12), 1300-1311. Van Hasselt, M. (2011). Bayesian inference in a sample selection model. Journal of Econometrics, 165(2), 221-232. ## Installation You can install the development version of TobitBART like so: ``` r library(devtools) install.packages("dbarts") install.packages("GIGrvg") install.packages("Rfast") install.packages("censReg") install.packages("accelerometry") install.packages("wrswoR") install.packages("dqrng") install_github("boennecd/fastncdf") install_github("EoghanONeill/TobitBART") ``` ## Example This is a basic example: ``` r library(TobitBART) ## basic example code #example taken from https://stats.idre.ucla.edu/r/dae/tobit-models/ dat <- read.csv("https://stats.idre.ucla.edu/stat/data/tobit.csv") train_inds <- sample(1:200,190) test_inds <- (1:200)[-train_inds] ytrain <- dat$apt[train_inds] ytest <- dat$apt[test_inds] xtrain <- cbind(dat$read, dat$math)[train_inds,] xtest <- cbind(dat$read, dat$math)[test_inds,] tobart_res <- tbart1(xtrain,xtest,ytrain, below_cens = -Inf, above_cens = 800, n.iter = 400, n.burnin = 100) ```

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：