Question

我正在使用R编写的包来将实验数据拟合到特定模型我想写自己的模型并使用相同的包（作者声称它是可能的）。

所以我正在挖掘源文件以找到他们定义模型函数的位置。我被卡住了他们使用nls的方式。我了解nls如何处理以下案例

x <- -(1:100)/10
y <- 100 + 10 * exp(x / 2) + rnorm(x)/2
nlmod <- nls(y ~  Const + A * exp(B * x),start=list(Const=5,A=5,B=7))

创建一个数据集并在其旁边编写模型 - 简单。

但是他们使用nls，如下所示：

>  nls(~rescomp(theta = t, d = d, currModel = currModel), 
>     data = list(d = vector(), currModel = currModel))

当我跑

时

>  ~rescomp(theta = t, d = d, currModel = currModel)

查看上例中的公式（Const + A * exp（B * x））。我明白了

~rescomp(theta = t, d = d, currModel = currModel) 

environment: 0x000000000a7f7530

我想了解如何查看生成的公式以及如何nls 数据设置为list \ environment时工作。有什么建议吗？

以下是rescomp

的内容

  function (theta = vector(), d = vector(), currModel = currModel, 
    currTheta = vector()) 
{
    if (length(currTheta) == 0) 
        currTheta <- getThetaCl(theta, currModel)
    groups <- currModel@groups
    m <- currModel@modellist
    resid <- clpindepX <- list()
    nexp <- length(m)
    for (i in 1:nexp) {
        clpindepX[[i]] <- if (!m[[i]]@clpdep || m[[i]]@getX) 
            getClpindepX(model = m[[i]], theta = currTheta[[i]], 
                multimodel = currModel, returnX = FALSE, rawtheta = theta, 
                dind = 0)
        else matrix()
    }
    for (i in 1:length(groups)) {
        resid[[i]] <- residPart(model = m[[1]], group = groups[[i]], 
            multimodel = currModel, thetalist = currTheta, clpindepX = clpindepX, 
            finished = currModel@finished, returnX = FALSE, rawtheta = theta)
        if (currModel@finished) {
            currModel <- fillResult(group = groups[[i]], multimodel = currModel, 
                thetalist = currTheta, clpindepX = clpindepX, 
                rlist = resid[[i]], rawtheta = theta)
        }
    }
    if (currModel@finished) {
        currModel@fit@nlsres$onls$nclp <- currModel@nclp
        if (currModel@optlist[[1]]@sumnls) {
            if (class(currModel@fit@nlsres$onls) == "nls") 
                class(currModel@fit@nlsres$onls) <- "timp.nls"
            else if (class(currModel@fit@nlsres$onls) == "nls.lm") 
                class(currModel@fit@nlsres$onls) <- "timp.nls.lm"
            else class(currModel@fit@nlsres$onls) <- "timp.optim"
            currModel@fit@nlsres$sumonls <- summary(currModel@fit@nlsres$onls, 
                currModel = currModel, currTheta = currTheta)
        }
        if (currModel@stderrclp) {
            for (i in 1:length(groups)) {
                currModel <- getStdErrClp(group = groups[[i]], 
                  multimodel = currModel, thetalist = currTheta, 
                  clpindepX = clpindepX, rlist = resid[[i]], 
                  rawtheta = theta) 
       }
        if (currModel@stderrclp) {
            for (i in 1:length(groups)) {
                currModel <- getStdErrClp(group = groups[[i]], 
                  multimodel = currModel, thetalist = currTheta, 
                  clpindepX = clpindepX, rlist = resid[[i]], 
                  rawtheta = theta)
            }
        }
    }
    if (currModel@finished && currModel@trilinear) {
        trires <- triResolve(currModel, currTheta)
        currModel <- trires$currModel
        currTheta <- trires$currTheta
    }
    if (currModel@finished && m[[1]]@mod_type == "kin") {
        if (m[[1]]@fullk) {
            for (i in 1:nexp) {
                nocolsums <- length(m[[1]]@lightregimespec) > 
                  0
                eig <- fullKF(currTheta[[i]]@kinpar, currTheta[[i]]@kinscal, 
                  m[[1]]@kmat, currTheta[[i]]@jvec, m[[1]]@fixedkmat, 
                  m[[1]]@kinscalspecial, m[[1]]@kinscalspecialspec, 
                  nocolsums)
                currTheta[[i]]@eigenvaluesK <- eig$values
            }
        }
    }
    if (currModel@finished) {
        return(list(currModel = currModel, currTheta = currTheta))
    }
    if (currModel@algorithm == "optim") 
        retval <- sum(unlist(resid))
    else retval <- unlist(resid)
    retval
}

Answer 1

没有公式。 nls重复调用一个函数，该函数引入'currModel'和参数（可能是theta和d），这些函数将根据rescomp函数返回的标量进行最小化。您输入“rescomp”的结果“太长而无法写在这里”的投诉只是表明a）您不理解您应该编辑您的问题而不是在评论中写出该输出，并且b）你对正在发生的事情的期望过于狭窄。

说明将第一个nls问题写成函数形式：

myfunc <- function(Const,A,B,y=y,x=x) { abs(y - ( Const + A * exp(B * x)))}

因此，您将最小化y与预测的绝对偏差：

nlmod <- nls( ~myfunc(Const,A,B,y=y,x=x) ,start=list(Const=5,A=5,B=7))
nlmod  # same results

nls函数和列表中的R函数

1 个答案: