Description Usage Arguments Details Value Author(s) References See Also Examples

Provide two sample versions (DEIF and SIF) of influence function on the AUC.

1 2 3 4 5 6 7 8 9 |

`score` |
A vector containing the predictions (continuous scores) assigned by classifiers; Must be numeric. |

`binary` |
A vector containing the true class labels 1: positive and 0: negative. Must have the same dimensions as 'score.' |

`threshold` |
A numeric value determining the threshold to distinguish influential observations from normal ones; Must lie between 0 and 1; Defaults to 0.5. |

`hypothesis` |
Logical which controls the evaluation of SIF under asymptotic distribution. |

`testdiff` |
A numeric value determining the difference in the hypothesis testing; Must lie between 0 and 1; Defaults to 0.5. |

`alpha` |
A numeric value determining the significance level in the hypothesis testing; Must lie between 0 and 1; Defaults to 0.05. |

`name` |
A vector comprising the appellations for observations; Must have the same dimensions as 'score'. |

Apply two sample versions of influence functions on AUC:

deleted empirical influence function (DEIF)

sample influence function (SIF)

The concept of influence function focuses on the deletion diagnostics; nevertheless, such techniques may face masking effect due to multiple influential observations.
To thoroughly investigate the potential cases in binary classification, we suggest end-users to apply `ICLC`

and `LAUC`

as well. For a complete discussion of these functions, please see the reference.

A list of objects including (1) 'output': a list of results with 'AUC' (numeric), 'SIF' (a list of dataframes) and 'DEIF' (a list of dataframes)); (2) 'rdata': a dataframe of essential results for visualization (3) 'threshold': a used numeric value to distinguish influential observations from normal ones; (4) 'test_output': a list of dataframes for hypothesis testing result; (5) 'test_data': a dataframe of essential results in hypothesis testing for visualization (6) 'testdiff': a used numeric value to determine the difference in the hypothesis testing; (7) 'alpha': a used nuermic value to determine the significance level.

Bo-Shiang Ke and Yuan-chin Ivan Chang

Ke, B. S., Chiang, A. J., & Chang, Y. C. I. (2018). Influence Analysis for the Area Under the Receiver Operating Characteristic Curve. Journal of biopharmaceutical statistics, 28(4), 722-734.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(ROCR)
data("ROCR.simple")
# print out IAUC results directly
IAUC(ROCR.simple$predictions,ROCR.simple$labels,hypothesis = "True")
data(mtcars)
glmfit <- glm(vs ~ wt + disp, family = binomial, data = mtcars)
prob <- as.vector( predict(glmfit, newdata = mtcars,type = "response"))
output <- IAUC(prob, mtcars$vs, threshold = 0.3, testdiff = 0.3,
hypothesis = TRUE, name = rownames(mtcars))
# Show results
print(output)
# Visualize results
plot(output)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.