从R caret包错误中训练函数： 所有精度指标值都丢失了”

Question

我想运行logreg回归。 在R上运行代码后，出现以下错误：

出了点问题； 所有精度指标值均缺失：

    Accuracy       Kappa    
 Min.   : NA   Min.   : NA  
 1st Qu.: NA   1st Qu.: NA  
 Median : NA   Median : NA  
 Mean   :NaN   Mean   :NaN  
 3rd Qu.: NA   3rd Qu.: NA  
 Max.   : NA   Max.   : NA  
 NA's   :9     NA's   :9    
Error in train.default(x, y, weights = w, ...) : Stopping
In addition: There were 19 warnings (use warnings() to see them)

这是我的代码：

## Data
donner <- read.delim("http://web.as.uky.edu/statistics/users/pbreheny/760/data/donner.txt")
set.seed(1234)
library(caret)
donner$Age <- as.numeric(donner$Age)
donner$Status <- as.factor(donner$Status)  
donner$Sex <- as.numeric(donner$Sex) 
splitIndex <- createDataPartition(donner$Status, p = .80, list = FALSE, times = 1)
trainDF <- donner[splitIndex,]
testDF <- donner[-splitIndex,]
ctrl <- trainControl(method = "cv", number = 2)
logregmodel <- train(Status ~ ., data = donner, method = "logreg", trControl = ctrl)

编辑1：

我将状态更改为二进制（0和1），但仍然有一些错误。 这是新代码：

## Data
donner <- read.delim("http://web.as.uky.edu/statistics/users/pbreheny/760/data/donner.txt")
set.seed(1234)
library(caret)
donner$Age <- as.numeric(donner$Age)
donner$Status <- as.integer(donner$Status)-1  
donner$Sex <- as.numeric(donner$Sex)-1 
splitIndex <- createDataPartition(donner$Status, p = .80, list = FALSE, times = 1)
trainDF <- donner[splitIndex,]
testDF <- donner[-splitIndex,]
ctrl <- trainControl(method = "cv", number = 2)
donner$Status <- as.factor(donner$Status)
logregmodel <- train(Status ~ ., data = donner, method = "logreg", trControl = ctrl)

Answer 1

只需修复您的数据即可。 逻辑回归-我假设您要的是逻辑回归，因为您调用了逻辑回归（ logreg ）方法，如果您想要的是Logit模型之类的东西，那整个问题就不重要了。首先是错误-仅适用于二进制变量， 并且不了解1和2可以表示二进制数据。 它需要字面0和1。

donner <- read.delim("http://web.as.uky.edu/statistics/users/pbreheny/760/data/donner.txt")
set.seed(1234)
library(caret)
donner$Age <- as.numeric(donner$Age)
donner$Status <- as.factor(donner$Status)  
donner$Sex <- as.numeric(donner$Sex) 
splitIndex <- createDataPartition(donner$Status, p = .80, list = FALSE, times = 1)
trainDF <- donner[splitIndex,]
testDF <- donner[-splitIndex,]
ctrl <- trainControl(method = "cv", number = 3)
donner$Status <- as.character(donner$Status)
donner$Status[!donner$Status == "Survived"] <- 0
donner$Status[donner$Status == "Survived"] <- 1
donner$Age_gr_mean <- 0
donner$Age_gr_mean[donner$Age_gr_mean > mean(donner$Age)] <- 1
donner$Age <- NULL
donner$Status <- as.numeric(donner$Status)
donner$Sex[donner$Sex == 2] <- 0
logregmodel <- train(Status ~ ., data = donner, method = "logreg", trControl = ctrl)

Answer 2

我从来没有使用过“ logreg”方法。 似乎有些行也没用。 这是我建议使用“ glm”作为方法。

## Data
donner <- read.delim("http://web.as.uky.edu/statistics/users/pbreheny/760/data/donner.txt")

set.seed(1234)
library(caret)

donner$Age <- as.numeric(donner$Age)
donner$Status <- as.factor(donner$Status)
donner$Sex <- as.numeric(donner$Sex)-1 

splitIndex <- createDataPartition(donner$Status, p = .80, list = FALSE, times = 1)
trainDF <- donner[splitIndex,]
testDF <- donner[-splitIndex,]

ctrl <- trainControl(method = "cv", number = 3)
logregmodel <- train(Status ~ ., data = trainDF, method = "glm", family='binomial', trControl = ctrl)

summary(logregmodel)