从R caret包错误中训练函数： 所有精度指标值都丢失了”

Question

I want to run a logreg regression. I obtain the following error after running my code on R:

Something is wrong; all the Accuracy metric values are missing:

    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)

Here is my code:

## 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)

EDIT 1:

I changed the status to binary (0 and 1) and I still have some errors. Here is the new code:

## 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

Just needed to fix your data. Logic Regression -- which is what I'm assuming you want, since you called the logic regression ( logreg ) method and this entire question is aside from the point if you're wanting something else like logit model, which would never give you the error in the first place -- is for binary variables only and it doesn't understand that 1's and 2's can represent binary data. It wants literal 0's and 1's.

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

I've personnaly never used the "logreg" method. It also seems that some lines are useless. Here is my suggestion using "glm" as a method.

## 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)