crmPack (version 1.0.0)

plot,Samples,DualEndpoint-method: 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 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

# NOT RUN {
# 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)


# }