After adjusting the template, create a vector with the predicted probabilities. For example,
predict(my, newdata = dataset, type = "response")
## 1 2 3 4 5 6 7 8
## 0.5939177 0.5464655 0.6365774 0.6446637 0.6239940 0.5303579 0.5328488 0.5466660
## 9 10 11 12 13 14 15 16
## 0.5503314 0.5417098 0.6458154 0.6415483 0.5260277 0.6029462 0.5471655 0.5380448
## 17 18 19 20 21 22 23 24
## 0.5302498 0.5838713 0.6385566 0.5903019 0.5627473 0.5493866 0.4831309 0.5645080
## 25 26 27 28 29 30
## 0.6018940 0.5328032 0.4676268 0.4963086 0.5694663 0.5800685
With this created probability vector, it is possible to establish any desired criterion so that the response variable is classified as a positive result. Below I set cutoff points at 0.1, 0.5 and 0.8:
ifelse(predict(my, newdata = dataset, type = "response") > 0.1, 1, 0)
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 28 29 30
## 1 1 1
ifelse(predict(my, newdata = dataset, type = "response") > 0.5, 1, 0)
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0
## 28 29 30
## 0 1 1
ifelse(predict(my, newdata = dataset, type = "response") > 0.8, 1, 0)
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 29 30
## 0 0 0