Train function from R caret package error: “Someth

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

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)

Answer 2

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)