devel/TestGPA.R

In 0liver0815/onc-crmpack-test: Object-Oriented Implementation of CRM Designs

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)))
plot(data)

model <- DualEndpointEmax(mu=c(0, 1),
                          Sigma=matrix(c(1, 0, 0, 1), nrow=2),
                          sigma2W=c(a=0.1, b=0.1),
                          rho=c(a=1, b=1),
                          E0=c(0,100),
                          Emax=c(0,500),
                          ED50=c(0,200),
                          refDose=500)

options <- McmcOptions(burnin=20000,
                       step=3,
                       samples=10000)
# be sure that mcmc class was loaded (otherwise conflict with coda package)
samples <- mcmc(data, model, options)

print(plot(samples, model, data, extrapolate=FALSE))
print(plot(samples, model, data, extrapolate=TRUE))

nextBest1 <- NextBestDualEndpoint(target=c(0.9,1.0),
                                  overdose=c(0.35, 1),
                                  maxOverdoseProb=0.25)

nextBest(nextBest=nextBest1,doselimit=1000,samples,model,data)

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")
options <- McmcOptions(burnin=20000,
                       step=3,
                       samples=10000)
# be sure that mcmc class was loaded (otherwise conflict with coda package)
samples <- mcmc(data, model, options)

nextBest1 <- NextBestDualEndpoint(target=c(0.9,1.0),
                                  overdose=c(0.35, 1),
                                  maxOverdoseProb=0.25)

nextBest(nextBest=nextBest1,doselimit=1000,samples,model,data)


# ---------------------------------
# Lets familiarize with the RW1
# ---------------------------------
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)))

model <- DualEndpointRW(mu=c(0, 1),
                        Sigma=matrix(c(1, 0, 0, 1), nrow=2),
                        sigma2betaW=
                            c(a=20,b=50),
                        sigma2W=
                            c(a=0.1, b=0.1),
                        rho=0.0,
                        smooth="RW1")

options <- McmcOptions(burnin=20000,
                       step=3,
                       samples=10000)
emptydata <- DataDual(doseGrid=data@doseGrid)
priorsamples <- mcmc(emptydata, model, options)