plot.BayesSurvive {BayesSurvive}

Create a plot of estimated coefficients

Description

Plot point estimates of regression coefficients and 95% credible intervals

Usage

## S3 method for class 'BayesSurvive'
plot(x, type = "mean", interval = TRUE, subgroup = 1, ...)

Arguments

x

an object of class BayesSurvive or a matrix. If x is a matrix, use BayesSurvive:::plot.BayesSurvive(x)

type

type of point estimates of regression coefficients. One of c("mean", "median"). Default is mean

interval

logical argument to show 95% credible intervals. Default is TRUE

subgroup

index of the subgroup for visualizing posterior coefficients

...

additional arguments sent to ggplot2::geom_point()

Value

ggplot object

Examples

library("BayesSurvive")
set.seed(123)

# Load the example dataset
data("simData", package = "BayesSurvive")

dataset <- list(
  "X" = simData[[1]]$X,
  "t" = simData[[1]]$time,
  "di" = simData[[1]]$status
)

# Initial value: null model without covariates
initial <- list("gamma.ini" = rep(0, ncol(dataset$X)))
# Hyperparameters
hyperparPooled <- list(
  "c0"     = 2, # prior of baseline hazard
  "tau"    = 0.0375, # sd for coefficient prior
  "cb"     = 20, # sd for coefficient prior
  "pi.ga"  = 0.02, # prior variable selection probability for standard Cox models
  "a"      = -4, # hyperparameter in MRF prior
  "b"      = 0.1, # hyperparameter in MRF prior
  "G"      = simData$G # hyperparameter in MRF prior
)


# run Bayesian Cox with graph-structured priors
fit <- BayesSurvive(
  survObj = dataset, hyperpar = hyperparPooled,
  initial = initial, nIter = 100
)

# show posterior mean of coefficients and 95% credible intervals
library("GGally")
plot(fit) +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, size = 7))