Saving output of confusionMatrix as a .csv table - r

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")

Answer 4

The absolute easiest solution is to simply write out using readr::write_rds. You can export and import all while keeping the confusionMatrix structure intact.

Answer 5

If A is a caret::confusionMatrix object, then:
broom::tidy(A) %>% writexl::write_xlsx("mymatrix.xlsx")
optionally replace writexl with write.csv().
To also include the table on a separate sheet:
broom::tidy(A) %>% list(as.data.frame(A$table)) %>% writexl::write_xlsx("mymatrix.xlsx")