Question

我已经按照crmPack插图（第16-17页）中提供的代码来定义单参数功率模型。我将需要使用更新功能为其提供一些毒性数据，但是我得到的错误是“没有名称为“ call”的插槽”。这是下面的代码。对于“解决”这个问题，我将不胜感激。非常感谢。

# package and options
library(crmPack)

options <- McmcOptions(burnin = 1000, step = 2, samples = 5000)

set.seed(1)

# extra functions to define the power model

.OneParExp <- setClass(Class = "OneParExp", contains = "Model",
 representation(skeletonFun = "function",
 skeletonProbs = "numeric",
 lambda = "numeric"))


OneParExp <- function(skeletonProbs, doseGrid, lambda)
 {
 skeletonFun <- approxfun(x = doseGrid, y = skeletonProbs, rule = 2)
 invSkeletonFun <- approxfun(x = skeletonProbs, y = doseGrid, rule = 1)

 .OneParExp(
 skeletonFun = skeletonFun, skeletonProbs = skeletonProbs,
 lambda = lambda,
 datamodel = function(){
 for (i in 1:nObs)
 {
 y[i] ~ dbern(p[i])
 p[i] <- skeletonProbs[xLevel[i]]^theta
 }},
 datanames = c("nObs", "y", "xLevel"),
 prob = function(dose, theta){ skeletonFun(dose)^theta },
 dose = function(prob, theta){ invSkeletonFun(prob^(1 / theta)) },
 priormodel = function(){ theta ~ dexp(lambda) },
 modelspecs = function(){ list(skeletonProbs = skeletonProbs,
 lambda = lambda) },
 init = function(){ list(theta = 1) }, sample = "theta")
 }


# tox data and model fitting

data <- Data(x = c(1.2,1.2,1.8,2.4,3),
                 y = c(0, 0, 0, 1, 1),
                 cohort = c(1, 1, 2, 3, 4),
                 doseGrid = seq(1.2, 3, 0.6),
                 ID = 1:5,
                 placebo = FALSE)

(skeletonProbs <- round(data@doseGrid / max(data@doseGrid) / 4, 2))

newModel <- OneParExp(skeletonProbs = skeletonProbs,
 doseGrid = data@doseGrid, lambda = 1)

newDLTmodel <- update(object=newModel, data=data)

Answer 1

您无需在此处使用“更新”功能来将数据输入模型。（“更新”方法主要是用于更新crmPack中“数据”对象的内部方法。）相反，您可以使用“ mcmc”来估计给定模型和数据的参数：

    estimates <- mcmc(model=newModel, data=data, options=McmcOptions())
    plot(estimates, newModel, data)

在crmPack中定义新模型，并使用“更新”功能向其提供新数据

1 个答案: