View source: R/riAFTBART_fit.R
riAFTBART_fit | R Documentation |
This function implements the random effect accelerated failure time BART (riAFT-BART) algorithm.
riAFTBART_fit( M.burnin, M.keep, M.thin = 1, status, y.train, x.train, trt.train, x.test, trt.test, cluster.id, verbose = FALSE, SA = FALSE, prior_c_function_used = NULL, gps = NULL )
M.burnin |
A numeric value indicating the number of MCMC iterations to be treated as burn in. |
M.keep |
A numeric value indicating the number of MCMC posterior draws after burn in. |
M.thin |
A numeric value indicating the thinning parameter. |
status |
A vector of event indicators: status = 1 indicates that the event was observed while status = 0 indicates the observation was right-censored. |
y.train |
A vector of follow-up times. |
x.train |
A dataframe or matrix, including all the covariates but not treatments for training data, with rows corresponding to observations and columns to variables. |
trt.train |
A numeric vector representing the treatment groups for the training data. If there's no treatment indicator, then set to |
x.test |
A dataframe or matrix, including all the covariates but not treatments for testing data, with rows corresponding to observations and columns to variables. |
trt.test |
A numeric vector representing the treatment groups for the testing data. If there's no treatment indicator, then set to |
cluster.id |
A vector of integers representing the clustering id. The cluster id should be an integer and start from 1. |
verbose |
A logical indicating whether to show the progress bar. The default is FALSE |
SA |
A logical indicating whether to conduct sensitivity analysis. The default is FALSE. |
prior_c_function_used |
Prior confounding functions used for SA, which is inherited from the sa function. The default is NULL. |
gps |
Generalized propensity score, which is inherited from the sa function. The default is NULL. |
A list with the following elements:
b: |
A matrix including samples from the posterior of the random effects. |
tree: |
A matrix with M.keep rows and nrow(x.train) columns represnting the predicted log survival time for x.train. |
tree.pred: |
A matrix with M.keep rows and nrow(x.test) columns represnting the predicted log survival time for x.test. |
tau: |
A vector representing the posterior samples of tau, the standard deviation of the random effects. |
sigma: |
A vector representing the posterior samples of sigma, the residual/error standard deviation. |
vip: |
A matrix with M.keep rows and ncol(x.train) columns represnting the variable inclusion proportions for each variable. |
library(riAFTBART) set.seed(20181223) n = 5 # number of clusters k = 50 # cluster size N = n*k # total sample size cluster.id = rep(1:n, each=k) tau.error = 0.8 b = stats::rnorm(n, 0, tau.error) alpha = 2 beta1 = 1 beta2 = -1 sig.error = 0.5 censoring.rate = 0.02 x1 = stats::rnorm(N,0.5,1) x2 = stats::rnorm(N,1.5,0.5) trt.train = sample(c(1,2,3), N, prob = c(0.4,0.3,0.2), replace = TRUE) trt.test = sample(c(1,2,3), N, prob = c(0.3,0.4,0.2), replace = TRUE) error = stats::rnorm(N,0,sig.error) logtime = alpha + beta1*x1 + beta2*x2 + b[cluster.id] + error y = exp(logtime) C = rexp(N, rate=censoring.rate) # censoring times Y = pmin(y,C) status = as.numeric(y<=C) res <- riAFTBART_fit(M.burnin = 10, M.keep = 10, M.thin = 1, status = status, y.train = Y, trt.train = trt.train, trt.test = trt.test, x.train = cbind(x1,x2), x.test = cbind(x1,x2), cluster.id = cluster.id)
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