extract AUC, NRI and IDI information from list of roc in pROC packages

ROC_table(ListModel, dec.auc = 3, dec.p = 3)

Arguments

ListModel

list of roc object

dec.auc

digits for AUC, Default: 3

dec.p

digits for p value, Default: 3

Value

table of AUC, NRI and IDI information

Details

extract AUC, NRI and IDI information from list of roc object in pROC packages.

See also

Examples

library(pROC)
#> Type 'citation("pROC")' for a citation.
#> #> Attaching package: ‘pROC’
#> The following objects are masked from ‘package:stats’: #> #> cov, smooth, var
m1 <- glm(vs ~ am + gear, data = mtcars, family = binomial) m2 <- glm(vs ~ am + gear + wt, data = mtcars, family = binomial) m3 <- glm(vs ~ am + gear + wt + mpg, data = mtcars, family = binomial) roc1 <- roc(m1$y, predict(m1, type = "response"))
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
roc2 <- roc(m2$y, predict(m2, type = "response"))
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
roc3 <- roc(m3$y, predict(m3, type = "response"))
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases
list.roc <- list(roc1, roc2, roc3) ROC_table(list.roc)
#> Prediction Model AUC 95% CI P-value for AUC Difference IDI #> <char> <num> <char> <num> <num> #> 1: Model 1 0.635 0.437-0.833 NA NA #> 2: Model 2 0.929 0.836-1 0.004 0.532 #> 3: Model 3 0.944 0.87-1 0.360 0.039 #> 95% CI P-value for IDI continuous NRI 95% CI P-value for NRI #> <char> <char> <num> <char> <char> #> 1: <NA> <NA> NA <NA> <NA> #> 2: 0.356-0.709 < 0.001 1.413 0.943-1.882 < 0.001 #> 3: -0.024-0.102 0.223 0.540 -0.12-1.199 0.109