roc: roc for model

In modelROC: Model Based ROC Analysis

Description

roc for model

Usage

roc(...)

## S3 method for class 'cph'
roc(..., times = NULL, model = NULL, x = NULL, method = c("NNE", "KM"))

## S3 method for class 'coxph'
roc(..., times = NULL, model = NULL, x = NULL, method = c("NNE", "KM"))

## S3 method for class 'glm'
roc(
  ...,
  negref = 0,
  model = NULL,
  x = NULL,
  method = c("empirical", "binormal", "nonparametric")
)

## S3 method for class 'lrm'
roc(
  ...,
  negref = 0,
  model = NULL,
  x = NULL,
  method = c("empirical", "binormal", "nonparametric")
)

Arguments

...	one or more fit
times	one or more times for cox regression
model	can be logical or characters. FALSE means no model TP and FP, characters mean model names.
x	can be logical or characters. TRUE means all x variable in regression will be calculated. One or more characters will be calculated only.
method	NNE or KM
negref	negative reference for each model

Value

roc dataframe

one roc_coxph for cox regression. model means model names,

References

Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics, 2000.

Pepe, Margaret Sullivan. The statistical evaluation of medical tests for classification and prediction. Medicine, 2003.

Zou, Kelly H., W. J. Hall, and David E. Shapiro. Smooth non-parametric receiver operating characteristic (ROC) curves for continuous diagnostic tests. Statistics in medicine 16, no. 19 (1997): 2143-2156.

modelROC documentation built on June 25, 2021, 5:09 p.m.