cal_metrics <- function(label, pred){
roc.p=pROC::roc(label, pred,ci =T)
if (roc.p$auc>0.5){
cutoff=roc.p$thresholds[which.max(roc.p$sensitivities+roc.p$specificities)]
sensitivity=roc.p$sensitivities[which.max(roc.p$sensitivities+roc.p$specificities)]
specificity=roc.p$specificities[which.max(roc.p$sensitivities+roc.p$specificities)]
ci = roc.p$ci
df=data.frame(type='positive classification',
auc=round(roc.p$auc,3),cutoff=cutoff,
sensitivity=sensitivity,specificity=specificity,ci_lower = ci[1],ci_upper=ci[3])
return(df)
}
else{
cutoff=roc.p$thresholds[which.min(roc.p$sensitivities+roc.p$specificities)]
sensitivity=roc.p$sensitivities[which.min(roc.p$sensitivities+roc.p$specificities)]
specificity=roc.p$specificities[which.min(roc.p$sensitivities+roc.p$specificities)]
ci = roc.p$ci
df=data.frame(type='negative classification',
auc=1-round(roc.p$auc,3),cutoff=cutoff,
sensitivity=1-sensitivity,specificity=1-specificity,ci_lower = ci[1],ci_upper=ci[3])
return(df)
}
}
