R

我试图线性优化R中的预测精度，我一直在寻找一个收敛点和一个方便的答案

我的想法如下：我有一组32个参数要优化。这32个参数是使用“rnorm”从正态分布中随机抽取的

linCoeff <- rnorm(32,0,5)

如果有人能帮上忙，我很乐意

---

提前感谢。

您可以尝试使用其他优化器吗？即，包

rgenoud

或

RcppDE

通常优于

optim

。我自己会用这些软件包为您做一些测试，但是由于您省略了

数据的任何值，因此示例实际上不可复制。
您好，非常感谢您的回答！可以考虑数据的每个列的值位于[-3；+3]范围，取自正态分布（难以给出所有数据集，但值接近分布）。您甚至可以在尝试中删除14和15项，有一些比例因子起初并不太有用。但至少，非常感谢，我会尝试这两个包，然后回来！
myVal <-  (((clSigm*lCoeff[1])+lCoeff[2])*data[,1])+
          (((clSigm*lCoeff[3])+lCoeff[4])*data[,2])+
          (((clSigm*lCoeff[5])+lCoeff[6])*data[,3])+
          (((clSigm*lCoeff[7])+lCoeff[8])*data[,4])+
          (((clSigm*lCoeff[9])+lCoeff[10])*data[,5])+
          (((clSigm*lCoeff[11])+lCoeff[12])*data[,6])+
          (((clSigm*lCoeff[13])+lCoeff[14])*data[,7])+
          (((clSigm*lCoeff[15])+lCoeff[16])*data[,8])+
          (((clSigm*lCoeff[17])+lCoeff[18])*data[,9])+
          (((clSigm*lCoeff[19])+lCoeff[20])*data[,10])+
          (((clSigm*lCoeff[21])+lCoeff[22])*data[,11])+
          (((clSigm*lCoeff[23])+lCoeff[24])*data[,12])+
          (((clSigm*lCoeff[25])+lCoeff[26])*data[,13])+
          (((clSigm*lCoeff[27])+lCoeff[28])*data[,14])*data$indDV1+
          (((clSigm*lCoeff[29])+lCoeff[30])*data[,15])*data$indDV2+
          ((clSigm*lCoeff[31])+lCoeff[32])

retrieveVal <- function(lCoeff,data){
  clSigm <- 1/(1+exp(.5-(data$acc)))
  myVal <- (((clSigm*lCoeff[1])+lCoeff[2])*data[,1])+
          (((clSigm*lCoeff[3])+lCoeff[4])*data[,2])+
          (((clSigm*lCoeff[5])+lCoeff[6])*data[,3])+
          (((clSigm*lCoeff[7])+lCoeff[8])*data[,4])+
          (((clSigm*lCoeff[9])+lCoeff[10])*data[,5])+
          (((clSigm*lCoeff[11])+lCoeff[12])*data[,6])+
          (((clSigm*lCoeff[13])+lCoeff[14])*data[,7])+
          (((clSigm*lCoeff[15])+lCoeff[16])*data[,8])+
          (((clSigm*lCoeff[17])+lCoeff[18])*data[,9])+
          (((clSigm*lCoeff[19])+lCoeff[20])*data[,10])+
          (((clSigm*lCoeff[21])+lCoeff[22])*data[,11])+
          (((clSigm*lCoeff[23])+lCoeff[24])*data[,12])+
          (((clSigm*lCoeff[25])+lCoeff[26])*data[,13])+
          (((clSigm*lCoeff[27])+lCoeff[28])*data[,14])*data$indDV1+
          (((clSigm*lCoeff[29])+lCoeff[30])*data[,15])*data$indDV2+
          ((clSigm*lCoeff[31])+lCoeff[32])
  act <- c(lapply(myVal,FUN=activate))
  return(-BACC(inp,act))
}

optim(par=linCoeff,fn=retrieveVal,data=myData)