R 如何使用库（插入符号）更改度量？

我想使用

 caret library

给出一些示例数据：

  ivar1<-rnorm(500, mean = 3, sd = 1)
  ivar2<-rnorm(500, mean = 4, sd = 1)
  ivar3<-rnorm(500, mean = 5, sd = 1)
  ivar4<-rnorm(500, mean = 4, sd = 1)
  dvar<-rpois(500, exp(3+ 0.1*ivar1 - 0.25*ivar2))

  data<-data.frame(dvar,ivar4,ivar3,ivar2,ivar1)



  ctrl <- rfeControl(functions=rfFuncs,
                  method="cv",
                  repeats = 5,
                  verbose = FALSE,
                  number=5)

model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl)

---

ivar1我不知道如何/是否可以轻松地将RMSE转换为RMSLE，因此您可以尝试更改控制功能

查看
rfFuncs$summary
它调用函数
postResample
。这是计算RMSE的地方-请查看该部分

mse <- mean((pred - obs)^2)
n <- length(obs)
out <- c(sqrt(mse), resamplCor^2)

然后定义新函数来计算RMSLE

mypostResample <- function (pred, obs) 
               {
               isNA <- is.na(pred)
               pred <- pred[!isNA]
               obs <- obs[!isNA]

               msle <- mean((log(pred) - log(obs))^2)
               out <- sqrt(msle)
               names(out) <- "RMSLE"

               if (any(is.nan(out))) 
                  out[is.nan(out)] <- NA
               out
               }

mypostResample我可以再问你一个问题吗：你如何在mypostResample函数中加入零的存在？我想你可以做
（log（1+obs）-log（1+pred））^2
。这就是软件包执行的检查功能
sle
。尽管如此，也许这是一个更适合统计人员的问题
msle <- mean((log(pred) - log(obs))^2)
out <- sqrt(msle)
}
names(out) <- "RMSLE"

newSumm <- function (data, lev = NULL, model = NULL) 
          {
          if (is.character(data$obs)) 
          data$obs <- factor(data$obs, levels = lev)
          mypostResample(data[, "pred"], data[, "obs"])
          }

mypostResample <- function (pred, obs) 
               {
               isNA <- is.na(pred)
               pred <- pred[!isNA]
               obs <- obs[!isNA]

               msle <- mean((log(pred) - log(obs))^2)
               out <- sqrt(msle)
               names(out) <- "RMSLE"

               if (any(is.nan(out))) 
                  out[is.nan(out)] <- NA
               out
               }

# keep old settings for future use
oldSumm <- rfFuncs$summary 

# update with new function
rfFuncs$summary <- newSumm

ctrl <- rfeControl(functions=rfFuncs,
                   method="cv",
                   repeats = 5,
                   verbose = FALSE,
                   number=5)
set.seed(1)
model <- rfe(data[,2:4], data[,1], sizes=c(1:4), rfeControl=ctrl, metric="RMSLE")

# plot
ggplot(model,type=c("g", "o"), metric="RMSLE")+ scale_x_continuous(breaks = 2:4, labels = names(data)[2:4])