在卡雷特需要真/假的地方缺少值

Question

我有一个数据框架，其中包含“日期变量”。(测试数据和代码可用这里)

但是，我使用"function = caretFunc“。它显示错误信息。

    Error in { : task 1 failed - "missing value where TRUE/FALSE needed"
In addition: Warning messages:
1: In FUN(newX[, i], ...) : NAs introduced by coercion
2: In FUN(newX[, i], ...) : NAs introduced by coercion
3: In FUN(newX[, i], ...) : NAs introduced by coercion
4: In FUN(newX[, i], ...) : NAs introduced by coercion
5: In FUN(newX[, i], ...) : NAs introduced by coercion
6: In FUN(newX[, i], ...) : NAs introduced by coercion
7: In FUN(newX[, i], ...) : NAs introduced by coercion
8: In FUN(newX[, i], ...) : NAs introduced by coercion
9: In FUN(newX[, i], ...) : NAs introduced by coercion
10: In FUN(newX[, i], ...) : NAs introduced by coercion

我能做什么?

复制错误的代码：

library(mlbench)
library(caret)
library(maps)
library(rgdal)
library(raster)
library(sp)
library(spdep)
library(GWmodel)
library(e1071)
library(plyr)
library(kernlab)
library(zoo)

mydata <- read.csv("Realestatedata_all_delete_date.csv", header=TRUE)
mydata$estate_TransDate <- as.Date(paste(mydata$estate_TransDate,1,sep="-"),format="%Y-%m-%d")
mydata$estate_HouseDate <- as.Date(mydata$estate_HouseDate,format="%Y-%m-%d")

rfectrl <- rfeControl(functions=caretFuncs, method="cv",number=10,verbose=TRUE,returnResamp = "final")
results <- rfe(mydata[,1:48],mydata[,49],sizes = c(1:48),rfeControl=rfectrl,method = "svmRadial")

print(results)
predictors(results)
plot(results, type=c("g", "o"))

Answer 1

在以下输入变量(向分类器提供的输入变量)中有NAs (缺失值)：

colnames(mydata)[unique(which(is.na(mydata[,1:48]), arr.ind = TRUE)[,2])]

给予：

 [1] "Aport_Distance"       "Univ_Distance"        "ParkR_Distance"
 [4] "TRA_StationDistance"  "THSR_StationDistance" "Schools_Distance"
 [7] "Lib_Distance"         "Sport_Distance"       "ParkS_Distance"
[10] "Hyper_Distance"       "Shop_Distance"        "Post_Distance"
[13] "Hosp_Distance"        "Gas_Distance"         "Incin_Distance"
[16] "Mort_Distance" 

此外，看起来您的日期变量(事务处理日期和豪宅日期)似乎在rfe(..)中被转换为rfe(..)。

支持向量机回归器似乎无法按原样处理NAs。

我会将日期转换为“自给定的参考日期以来的年份”：

mydata$estate_TransAge <- as.numeric(as.Date("2015-11-01") - mydata$estate_TransDate) / 365.25
mydata$estate_HouseAge <- as.numeric(as.Date("2015-11-01") - mydata$estate_HouseDate) / 365.25

# define the set of input variables
inputVars = setdiff(colnames(mydata),

                    # exclude these
                    c("estate_TransDate", "estate_HouseDate", "estate_TotalPrice")
                   )

并删除作为回归器输入的任何列中的带有任何NA的条目：

traindata <- mydata[complete.cases(mydata[,inputVars]),]

然后使用以下命令运行rfe：

rfectrl <- rfeControl(functions=caretFuncs, method="cv",number=10,verbose=TRUE,returnResamp = "final")
results <- rfe(
               traindata[,inputVars], 
               traindata[,"estate_TotalPrice"],
               rfeControl=rfectrl,
               method = "svmRadial"
              )

在我的例子中，这需要很长时间才能完成，所以我只在1%的数据上使用以下方法进行了测试：

traindata <- sample_frac(traindata, 0.01)

问题仍然是，如果给您的数据，以预测价格的一些输入变量如NA。