crmPack (version 2.0.0)

fitGain: Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Description

Get the fitted values for the gain values at all dose levels based on a given pseudo DLE model, DLE sample, a pseudo efficacy model, a Efficacy sample and data. This method returns a data frame with dose, middle, lower and upper quantiles of the gain value samples

Usage

fitGain(DLEmodel, DLEsamples, Effmodel, Effsamples, data, ...)
# S4 method for ModelTox,Samples,ModelEff,Samples,DataDual
fitGain(
  DLEmodel,
  DLEsamples,
  Effmodel,
  Effsamples,
  data,
  points = data@doseGrid,
  quantiles = c(0.025, 0.975),
  middle = mean,
  ...
)

Arguments

DLEmodel

the DLE pseudo model of ModelTox class object

DLEsamples

the DLE samples of Samples class object

Effmodel

the efficacy pseudo model of ModelEff class object

Effsamples

the efficacy samples of Samples class object

data

the data input of DataDual class object

...

additional arguments for methods

points

at which dose levels is the fit requested? default is the dose grid

quantiles

the quantiles to be calculated (default: 0.025 and 0.975)

middle

the function for computing the middle point. Default: mean

Functions

• fitGain( DLEmodel = ModelTox, DLEsamples = Samples, Effmodel = ModelEff, Effsamples = Samples, data = DataDual ): This method returns a data frame with dose, middle, lower, upper quantiles for the gain values obtained given the DLE and the efficacy samples

Examples

##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)
##Obtain the 'fitGain' the middle, uppper and lower quantiles for the samples of gain values
## at all dose levels using a pseudo DLE model, a DLE sample, a pseudo Efficacy model and
## a efficacy sample
## data must be from 'DataDual' class
data <- DataDual(
  x = c(25, 50, 25, 50, 75, 300, 250, 150),
  y = c(0, 0, 0, 0, 0, 1, 1, 0),
  w = c(0.31, 0.42, 0.59, 0.45, 0.6, 0.7, 0.6, 0.52),
  doseGrid = seq(25, 300, 25),
  placebo = FALSE
)
## DLE model must be from 'ModelTox' class e.g using 'LogisticIndepBeta' model
DLEmodel <- LogisticIndepBeta(
  binDLE = c(1.05, 1.8),
  DLEweights = c(3, 3),
  DLEdose = c(25, 300),
  data = data
)

## Efficacy model must be from 'ModelEff' class e.g using 'Effloglog' model
Effmodel <- Effloglog(
  c(1.223, 2.513),
  c(25, 300),
  nu = c(a = 1, b = 0.025),
  data = data,
  c = 0
)
## samples must be from 'Samples' class (object slot in fit)
options <- McmcOptions(burnin = 100, step = 2, samples = 200)
##set up the same data set in class 'Data' for MCMC sampling for DLE
data1 <- Data(x = data@x, y = data@y, doseGrid = data@doseGrid)

DLEsamples <- mcmc(data = data1, model = DLEmodel, options = options)
Effsamples <- mcmc(data = data, model = Effmodel, options = options)

fitGain(
  DLEmodel = DLEmodel,
  DLEsamples = DLEsamples,
  Effmodel = Effmodel,
  Effsamples = Effsamples,
  data = data
)