plot.riAFTBART_estimate: Plot the trace plots for the parameters from a fitted...

In riAFTBART: A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes

Plot the trace plots for the parameters from a fitted riAFT-BART model

Description

This function creates the trace plots for the parameters from a fitted riAFT-BART model.

Usage

## S3 method for class 'riAFTBART_estimate'
plot(x, focus = "sigma", id = NULL, ...)

Arguments

x	A fitted object of from riAFTBART_fit function.
focus	A character specifying which parameter to plot.
id	A numeric vector indicating the subject or cluster index to plot, when the object to plot is random intercepts or predicted log survival time.
...	further arguments passed to or from other methods.

Value

A plot

Examples

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)
plot(x = res, focus = "sigma")

riAFTBART documentation built on May 17, 2022, 1:07 a.m.