Entering edit mode

3.4 years ago

svlachavas
▴
730

Dear Community,

with the initial purpose of comparing 3 groups of different features, in the same set of samples, regarding a binary categorical outcome in a microarray dataset, i used the same algorithm in R (random forests) with the train() function, as with the same random seed, like the following illustrative example:

```
set.seed(1)
t.group1 <- train(x=group1, y=outcome, method = "rf", trControl = control,metric = "ROC")
set.seed(1)
t.group2 <- train(x=group2, y=outcome, method = "rf", trControl = control,metric = "ROC")
set.seed(1)
t.group3 <- train(x=group3, y=outcome, method = "rf", trControl = control,metric = "ROC")
models <- list(group1 = t.group1, group2=t.group2, group3 =t.group3)
resample_results <- resamples(models)
difValues <- diff(resample_results)
summary(difValues)
Call:
summary.diff.resamples(object = difValues)
p-value adjustment: bonferroni
Upper diagonal: estimates of the difference
Lower diagonal: p-value for H0: difference = 0
ROC
group1 group2 group3
group1 -0.01056 -0.06222
group2 1.0000000 -0.05167
group3 8.857e-08 0.0004048
Sens
group1 group2 group3
group1 0.03333 -0.02333
group2 0.72149 -0.05667
group3 0.75545 0.03167
Spec
group1 group2 group3
group1 -0.060000 -0.066667
group2 0.1978 -0.006667
group3 2.623e-05 1.0000
```

My main confussion here, is about the mentioned upper and lower diagonals in the caret package-in other words, how here i could inspect which are the relative adjusted p-values about the comparisons of each model/group feature, and if there differences ?