Saving output of confusionMatrix as a .csv table

Question

I have a following code resulting in a table-like output

 lvs <- c("normal", "abnormal")
 truth <- factor(rep(lvs, times = c(86, 258)),
                 levels = rev(lvs))
 pred <- factor(
                c(
                  rep(lvs, times = c(54, 32)),
                  rep(lvs, times = c(27, 231))),               
                levels = rev(lvs))

 xtab <- table(pred, truth)

 library(caret)
 confusionMatrix(xtab)

 confusionMatrix(pred, truth)
 confusionMatrix(xtab, prevalence = 0.25)   

I would like to export the below part of the output as a .csv table

               Accuracy : 0.8285          
                 95% CI : (0.7844, 0.8668)
    No Information Rate : 0.75            
    P-Value [Acc > NIR] : 0.0003097       

                  Kappa : 0.5336          
 Mcnemar's Test P-Value : 0.6025370       

            Sensitivity : 0.8953          
            Specificity : 0.6279          
         Pos Pred Value : 0.8783          
         Neg Pred Value : 0.6667          
             Prevalence : 0.7500          
         Detection Rate : 0.6715          
   Detection Prevalence : 0.7645          
      Balanced Accuracy : 0.7616  

Attempt to write it as a .csv table results in the error message:

write.csv(confusionMatrix(xtab),file="file.csv")
Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : 
cannot coerce class ""confusionMatrix"" to a data.frame

Doing the whole work manually, for obvious reasons, is impractical and prone to human errors.

Any suggestions on how to export it as a .csv?

Answer 1

Using caret package

results <- confusionMatrix(pred, truth)

as.table(results) gives

         Reference
Prediction  X1  X0
        X1  36  29
        X0 218 727

as.matrix(results,what="overall") gives

Accuracy       7.554455e-01
Kappa          1.372895e-01
AccuracyLower  7.277208e-01
AccuracyUpper  7.816725e-01
AccuracyNull   7.485149e-01
AccuracyPValue 3.203599e-01
McnemarPValue  5.608817e-33

and as.matrix(results, what = "classes") gives

Sensitivity          0.8953488
Specificity          0.6279070
Pos Pred Value       0.8783270
Neg Pred Value       0.6666667
Precision            0.8783270
Recall               0.8953488
F1                   0.8867562
Prevalence           0.7500000
Detection Rate       0.6715116
Detection Prevalence 0.7645349
Balanced Accuracy    0.7616279

Using these and write.csv command you can get the entire confusionMatrix info

Answer 2

Ok, so if you inspect the output of confusionMatrix(xtab, prevalence = 0.25) , it's a list:

cm <- confusionMatrix(pred, truth)
str(cm)

    List of 5
 $ positive: chr "abnormal"
 $ table   : 'table' int [1:2, 1:2] 231 27 32 54
  ..- attr(*, "dimnames")=List of 2
  .. ..$ Prediction: chr [1:2] "abnormal" "normal"
  .. ..$ Reference : chr [1:2] "abnormal" "normal"
 $ overall : Named num [1:7] 0.828 0.534 0.784 0.867 0.75 ...
  ..- attr(*, "names")= chr [1:7] "Accuracy" "Kappa" "AccuracyLower" "AccuracyUpper" ...
 $ byClass : Named num [1:8] 0.895 0.628 0.878 0.667 0.75 ...
  ..- attr(*, "names")= chr [1:8] "Sensitivity" "Specificity" "Pos Pred Value" "Neg Pred Value" ...
 $ dots    : list()
 - attr(*, "class")= chr "confusionMatrix"

From here on you select the appropriate objects that you want to create a csv from and make a data.frame that will have a column for each variable. In your case, this will be:

tocsv <- data.frame(cbind(t(cm$overall),t(cm$byClass)))

# You can then use
write.csv(tocsv,file="file.csv")

Answer 3

I found that capture.output works best for me.

It simply copies your output as a .csv file

(you can also do it as .txt)

capture.output(
confusionMatrix(xtab, prevalence = 0.25),
 file = "F:/Home Office/result.csv")