diff --git a/NAMESPACE b/NAMESPACE
index d35d82547..f3c9e008a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -26,6 +26,7 @@ S3method(as.data.frame,r2_bayes)
 S3method(as.data.frame,r2_loo)
 S3method(as.data.frame,r2_nakagawa)
 S3method(as.numeric,check_outliers)
+S3method(as.numeric,performance_roc)
 S3method(check_autocorrelation,default)
 S3method(check_collinearity,BFBayesFactor)
 S3method(check_collinearity,MixMod)
diff --git a/R/performance_roc.R b/R/performance_roc.R
index 5cdd8d17f..e7d0f0006 100644
--- a/R/performance_roc.R
+++ b/R/performance_roc.R
@@ -46,6 +46,7 @@
 #'
 #' model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
 #' as.data.frame(performance_roc(model, new_data = test_data))
+#' as.numeric(performance_roc(model))
 #'
 #' roc <- performance_roc(model, new_data = test_data)
 #' area_under_curve(roc$Specificity, roc$Sensitivity)
@@ -122,6 +123,21 @@ print.performance_roc <- function(x, ...) {
 }
 
 
+#' @export
+as.numeric.performance_roc <- function(x, ...) {
+  if (length(unique(x$Model)) == 1) {
+    auc <- bayestestR::area_under_curve(x$Specificity, x$Sensitivity)
+  } else {
+    dat <- split(x, f = x$Model)
+
+    auc <- c()
+    for (i in seq_along(dat)) {
+      auc <- c(auc, bayestestR::area_under_curve(dat[[i]]$Specificity, dat[[i]]$Sensitivity))
+    }
+  }
+  auc
+}
+
 
 # utilities ---------------------------
 
@@ -181,5 +197,5 @@ print.performance_roc <- function(x, ...) {
   if (inherits(x, "model_fit")) {
     x <- x$fit
   }
-  inherits(x, c("glm", "glmerMod", "logitor", "logitmfx", "probitmfx"))
+  inherits(x, c("glm", "glmerMod", "logitor", "logitmfx", "probitmfx", "glmmTMB"))
 }
diff --git a/man/performance_roc.Rd b/man/performance_roc.Rd
index 1e116af70..48b473a42 100644
--- a/man/performance_roc.Rd
+++ b/man/performance_roc.Rd
@@ -59,6 +59,7 @@ train_data <- iris[-folds, ]
 
 model <- glm(y ~ Sepal.Length + Sepal.Width, data = train_data, family = "binomial")
 as.data.frame(performance_roc(model, new_data = test_data))
+as.numeric(performance_roc(model))
 
 roc <- performance_roc(model, new_data = test_data)
 area_under_curve(roc$Specificity, roc$Sensitivity)