使用插入符号创建训练和测试数据时缺失值

Question

我的问题是在使用 train 来拟合带插入符号的模型时如何处理缺失值。我的一小部分数据样本如下：

       df <- dput(dat)
       structure(list(LagO3 = c(NA, NA, NA, 40, 45, NA), RH = c(69.4087524414062, 
       79.9608383178711, 64.4592437744141, 66.4207077026367, 66.0899200439453, 
       91.3353729248047), SR = c(298.928888888889, 300.128888888889, 
       303.688888888889, 304.521111111111, 303.223333333333, 294.716666666667
       ), ST = c(317.9917578125, 317.448253038194, 311.039059244792, 
       312.557927517361, 321.252841796875, 330.512212456597), Tmx = c(294.770359293045, 
       294.897191864461, 295.674552786042, 296.247345044048, 296.108238352818, 
       294.594430242372), CWTE = c(0, 1, 0, 0, 0, 0), CWTW = c(0, 0, 
       0, 0, 0, 0), o3 = c(NA, NA, NA, 52, 55, NA)), .Names = c("LagO3", 
       "RH", "SR", "ST", "Tmx", "CWTE", "CWTW", "o3"), row.names = c("1", 
       "2", "3", "4", "5", "6"), class = "data.frame")

问题是，对于我的一个预测变量中的几个位置，我有 NA，而预测变量 (o3) 也有 NA（但在不同的位置）。然后，我尝试了：

model <- train(x = na.omit(x.training), y = na.omit(training$o3), method = "lmStepAIC",
               direction="backward", trControl = control)

但是，我会有不同的长度... 我尝试使用：

 model <- train(x = x.training, y = training$o3,na.action=na.pass, 
                method = "lmStepAIC",direction="backward",trControl = control)

出现以下错误：

Error in quantile.default(y, probs = seq(0, 1, length = cuts)) : 
  missing values and NaN's not allowed if 'na.rm' is FALSE

如有任何建议，我将不胜感激！

非常感谢。

Answer 1

您需要将 na.action 参数与 train 函数的 na.omit 一起使用。正如文档所说 na.action（类型 ?train）：

A function to specify the action to be taken if NAs are found. The default action is for the procedure to fail. An alternative is na.omit, which leads to rejection of cases with missing values on any required variable. (NOTE: If given, this argument must be named.)

因此以下内容将起作用：

model <- train(x = x.training, y = training$o3, 
              method = "lmStepAIC",direction="backward", 
              trControl = control, na.action=na.omit)

输出：

> model <- train(x = x.training, y = y.training, method = "lmStepAIC",direction="backward",
+                na.action=na.omit)
Start:  AIC=-129.7
.outcome ~ LagO3 + RH + SR + ST + Tmx + CWTE + CWTW


Step:  AIC=-129.7
.outcome ~ LagO3 + RH + SR + ST + Tmx + CWTE


Step:  AIC=-129.7
.outcome ~ LagO3 + RH + SR + ST + Tmx


Step:  AIC=-129.7
.outcome ~ LagO3 + RH + SR + ST


Step:  AIC=-129.7
.outcome ~ LagO3 + RH + SR


Step:  AIC=-129.7
.outcome ~ LagO3 + RH


Step:  AIC=-129.7
.outcome ~ LagO3


Step:  AIC=-129.7
.outcome ~ 1
...
...
...

使用插入符号创建训练和测试数据时缺失值

missing values when creating training and testing data with caret

statistics

r

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