identifying miss classified values in confusion matrix in R - r

Question

I am using the caret package along with the confusionMatrix function and I would like to know if it is possible to know which are the exact values that were not clasified properly.
Here is a subset of my train data
train_sub <- structure(
list(
corr = c(
0.629922866893549,
0.632354159559817,
0.656112138936032,
0.4469719807955,
0.598136079870775,
0.314461239093862,
0.379065842199838,
0.347331370037428,
0.310270891798492,
0.361064451331448,
0.335628455451358
),
rdist = c(
0.775733824285612,
0.834148208687529,
0.884167982488944,
0.633989717138057,
0.850225777237626,
0.626197919283803,
0.649597055761598,
0.680382136363523,
0.627828985862852,
0.713674404108905,
0.646094473468118
),
CCF2 = c(
0.634465565134314,
0.722096802135009,
0.792385621105087,
0.46497582143802,
0.739612023831014,
0.470724554509749,
0.505961260826622,
0.527876803999064,
0.461724328071479,
0.564117580569802,
0.490084457081904
),
Wcorr = c(
0.629,
0.613,
0.812,
0.424,
0.593,
0.36,
0.346,
0.286,
0.333,
0.381,
0.333
),
Wcorr2 = c(
0.735,
0.743,
0.802,
0.588,
0.691,
0.632,
0.61,
0.599,
0.599,
0.632,
0.613
),
Wcorr3 = c(
0.21,
0.301,
0.421,
-0.052,
0.169,
-0.032,
-0.042,-0.048,
-0.035,
0.006,
-0.004
),
Var = c("W", "W", "W", "W",
"W", "B", "B", "B", "B", "B", "B")
),
row.names = c(1L, 2L,
3L, 5L, 7L, 214L, 215L, 216L, 217L, 218L, 221L),
class = "data.frame"
)
and here is a subset of my test data
test_sub <- structure(
list(
corr = c(
0.636658204667785,
0.5637857758104,
0.540558984461647,
0.392647603023863,
0.561801911406989,
0.297187412065481,
0.278864501603015,
0.505277007007347,
0.403811785308709,
0.510158398354856,
0.459607853624603
),
rdist = c(
0.887270722679019,
0.843656768956754,
0.815806338767273,
0.732093571145576,
0.832944903081762,
0.485497073465096,
0.454461718498521,
0.69094669881886,
0.627667080657035,
0.705558894672344,
0.620838398507191
),
CCF2 = c(
0.802017782695131,
0.731763898271157,
0.689402284804853,
0.577932997250877,
0.715111899030751,
0.324826043263382,
0.298456267077388,
0.544808216945995,
0.458148923874818,
0.551160266327893,
0.461228649848996
),
Wcorr = c(
0.655,
0.536,
0.677,
0.556,
0.571,
0.29,
0.25,
0.484,
0.25,
0.515,
0.314
),
Wcorr2 = c(
0.779,
0.682,
0.734,
0.675,
0.736,
0.5,
0.529,
0.611,
0.555,
0.639,
0.572
),
Wcorr3 = c(
0.368,
0.154,
0.266,
0.103,
0.224,
-0.204,
-0.16,
-0.026,
-0.149,
0.032,
-0.097
),
Var = c("W", "W", "W", "W", "W", "B", "B", "B", "B", "B",
"B")
),
row.names = c(4L, 6L, 8L, 13L, 15L, 321L, 322L, 329L,
334L, 341L, 344L),
class = "data.frame"
)
When I use this line,
confusionMatrix(reference=as.factor(test$Var),data=fittedTL,mode = "everything")
With this I compute some machine learning using glmnet method (it gives the best accuracy ini my case)
classCtrl <- trainControl(method = "repeatedcv", number=10,repeats=5,classProbs = TRUE,savePredictions = "final")
set.seed(355)
glmnetTL <- train(Var~., train_sub, method= "glmnet", trControl=classCtrl)
glmnetTL
And finally I compute the confusion matrix on my test set:
predict_glmnet <- predict(glmnetTL,test_sub)
predict_glmnet
CM_glmnet <- confusionMatrix(reference=as.factor(test_sub$Var),data=predict_glmnet,mode = "everything")
CM_glmnet
The output of the confusion matrix is a table like so
B
W
B
4
0
W
2
5
So here I have two predictions/classifications that are not good.
Is there any way I can traceback to which row of my test set it corresponds ?