plot-Samples-DualEndpoint-method: Plotting dose-toxicity and dose-biomarker model fits

In crmPack: Object-Oriented Implementation of CRM Designs

Plotting dose-toxicity and dose-biomarker model fits

Description

When we have the dual endpoint model, also the dose-biomarker fit is shown in the plot

Usage

## S4 method for signature 'Samples,DualEndpoint'
plot(x, y, data, extrapolate = TRUE, showLegend = FALSE, ...)

Arguments

x	the Samples object
y	the DualEndpoint object
data	the DataDual object
extrapolate	should the biomarker fit be extrapolated to the whole dose grid? (default)
showLegend	should the legend be shown? (not default)
...	additional arguments for the parent method plot,Samples,Model-method

Value

This returns the ggplot object with the dose-toxicity and dose-biomarker model fits

Examples

# Create some data
data <- DataDual(
  x=c(0.1, 0.5, 1.5, 3, 6, 10, 10, 10,
      20, 20, 20, 40, 40, 40, 50, 50, 50),
  y=c(0, 0, 0, 0, 0, 0, 1, 0,
      0, 1, 1, 0, 0, 1, 0, 1, 1),
  w=c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.55, 0.6,
      0.52, 0.54, 0.56, 0.43, 0.41, 0.39, 0.34, 0.38, 0.21),
  doseGrid=c(0.1, 0.5, 1.5, 3, 6,
             seq(from=10, to=80, by=2)))

# Initialize the Dual-Endpoint model (in this case RW1)
model <- DualEndpointRW(mu = c(0, 1),
                        Sigma = matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW = 0.01,
                        sigma2W = c(a=0.1, b=0.1),
                        rho = c(a=1, b=1),
                        smooth = "RW1")

# Set-up some MCMC parameters and generate samples from the posterior
options <- McmcOptions(burnin=100,
                       step=2,
                       samples=500)
set.seed(94)
samples <- mcmc(data, model, options)

# Plot the posterior mean  (and empirical 2.5 and 97.5 percentile)
# for the prob(DLT) by doses and the Biomarker by doses
#grid.arrange(plot(x = samples, y = model, data = data))
              
plot(x = samples, y = model, data = data)

crmPack documentation built on Sept. 3, 2022, 1:05 a.m.