Introduce

pROC In bag roc() Can draw the classification problem roc curve , Created is S3 object ;ROCR Packages can also be drawn roc curve , But what was created was S4 object .

roc() Function introduction

roc(...)
## S3 method for class 'formula'
roc(formula, data, ...)
## S3 method for class 'data.frame'
roc(data, response, predictor,
ret = c("roc", "coords", "all_coords"), ...)
## Default S3 method:
roc(response, predictor, controls, cases,
density.controls, density.cases,
levels=base::levels(as.factor(response)), percent=FALSE, na.rm=TRUE,
direction=c("auto", "<", ">"), algorithm = 6, quiet = FALSE, 
smooth=FALSE, auc=TRUE, ci=FALSE, plot=FALSE, smooth.method="binormal",
smooth.n=512, ci.method=NULL, density=NULL, ...)
roc_(data, response, predictor, ret = c("roc", "coords", "all_coords"), ...)

Parameter Introduction

• response
  Is the real classification
  a factor, numeric or character vector of responses (true class), typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve. If the vector contains more than two unique values, or if their order could be ambiguous, use levels to specify which values must be used as control and case value. If the first argument was a data.frame, response should be the name of the column in data containing the response, quoted for roc_, and optionally quoted for roc.data.frame (non-standard evaluation or NSE).

• predictor
  The probability of prediction
  a numeric or ordered vector of the same length than response, containing the predicted value of each observation. If the first argument was a data.frame, predictor should be the name of the column in data containing the predictor, quoted for roc_, and optionally quoted for roc.data.frame (non-standard evaluation or NSE).

• controls, cases
  instead of response, predictor, the data can be supplied as two numeric or ordered vectors containing the predictor values for control and case observations.

• density.controls, density.cases
  a smoothed ROC curve can be built directly from two densities on identical x points, as in smooth.

• formula, data
  a formula of the type response~predictor. If mulitple predictors are passed, a named list of roc objects will be returned. Additional arguments data and subset, but not na.action are supported, see model.frame for more details.

• levels
  the value of the response for controls and cases respectively. By default, the first two values of levels(as.factor(response)) are taken, and the remaining levels are ignored. It usually captures two-class factor data correctly, but will frequently fail for other data types (response factor with more than 2 levels, or for example if your response is coded “controls” and “cases”, the levels will be inverted) and must then be specified here. If your data is coded as 0 and 1 with 0 being the controls, you can safely omit this argument.

• percent
  if the sensitivities, specificities and AUC must be given in percent (TRUE) or in fraction (FALSE, default).

• na.rm
  if TRUE, the NA values will be removed (ignored by roc.formula).

• direction
  in which direction to make the comparison? “auto” (default): automatically define in which group the median is higher and take the direction accordingly. “>”: if the predictor values for the control group are higher than the values of the case group (controls > t >= cases). “<”: if the predictor values for the control group are lower or equal than the values of the case group (controls < t <= cases). You should set this explicity to “>” or “<” whenever you are resampling or randomizing the data, otherwise the curves will be biased towards higher AUC values.

• algorithm
  the method used to compute sensitivity and specificity, an integer of length 1 between 0 and 6. 1: a safe, well-tested, pure-R code that is efficient when the number of thresholds is low. It goes with O(T*N). 2: an alternative pure-R algorithm that goes in O(N). Typically faster than 1 when the number of thresholds of the ROC curve is above 1000. Less tested than 1. 3: a C++ implementation of 1, about 3-5x faster. Typically the fastest with ROC curves with less than 50-100 thresholds, but has a very bad worst-case when that number increases. 4 (debug only, slow): runs algorithms 1 to 3 and makes sure they return the same values. 5: select 2 or 3 based on the number of thresholds. 6 (default): quickly select the algorithm on the class of the data: 2 for numeric and 3 for ordered. 0: use microbenchmark to choose between 2 and 3.

• ret
  for roc.data.frame only, whether to return the threshold sensitivity and specificity at all thresholds (“coords”), all the coordinates at all thresholds (“all_coords”) or the roc object (“roc”).

• quiet
  set to TRUE to turn off messages when direction and levels are auto-detected.

• smooth
  if TRUE, the ROC curve is passed to smooth to be smoothed.

• auc
  compute the area under the curve (AUC)? If TRUE (default), additional arguments can be passed to auc.

• ci
  compute the confidence interval (CI)? If set to TRUE, additional arguments can be passed to ci.

• plot
  plot the ROC curve? If TRUE, additional arguments can be passed to plot.roc.

• smooth.method, smooth.n, ci.method
  in roc.formula and roc.default, the method and n arguments to smooth (if smooth=TRUE) and of=“auc”) must be passed as smooth.method, smooth.n and ci.method to avoid confusions.

• density
  density argument passed to smooth.

• …
  further arguments passed to or from other methods, and especially:

  • auc: partial.auc, partial.auc.focus, partial.auc.correct.

  • ci: of, conf.level, boot.n, boot.stratified, progress

  • ci.auc:, reuse.auc, method

  • ci.thresholds: thresholds

  • ci.se: sensitivities

  • ci.sp: specificities

  • plot.roc: add, col and most other arguments to the plot.roc function. See plot.roc directly for more details.

  • smooth: method, n, and all other arguments. See smooth for more details.

Return value

• auc
  if called with auc=TRUE, a numeric of class “auc” as defined in auc.

• ci
  if called with ci=TRUE, a numeric of class “ci” as defined in ci.

• response
  the response vector. Patients whose response is not %in% levels are discarded. If NA values were removed, a na.action attribute similar to na.omit stores the row numbers.

• predictor
  the predictor vector converted to numeric as used to build the ROC curve. Patients whose response is not %in% levels are discarded. If NA values were removed, a na.action attribute similar to na.omit stores the row numbers.

• original.predictor, original.response
  the response and predictor vectors as passed in argument.

• levels
  the levels of the response as defined in argument.

• controls
  the predictor values for the control observations.

• cases
  the predictor values for the cases.

• percent
  if the sensitivities, specificities and AUC are reported in percent, as defined in argument.

• direction
  the direction of the comparison, as defined in argument.

• fun.sesp
  the function used to compute sensitivities and specificities. Will be re-used in bootstrap operations.

• sensitivities
  the sensitivities defining the ROC curve.

• specificities
  the specificities defining the ROC curve.

• thresholds
  the thresholds at which the sensitivities and specificities were computed. See below for details.

• call
  how the function was called. See match.call for more details.

example

data(aSAH)

# With a formula:
roc(outcome ~ s100b, data=aSAH)
# Call:
# roc.formula(formula = outcome ~ s100b, data = aSAH)
# 
# Data: s100b in 72 controls (outcome Good) < 41 cases (outcome Poor).
# Area under the curve: 0.7314


# Plot and CI (see plot.roc and ci for more options):
roc(aSAH$outcome, aSAH$s100b,
    percent=TRUE, plot=TRUE, ci=TRUE)

# Smoothed ROC curve
roc(aSAH$outcome, aSAH$s100b, plot=TRUE,smooth=TRUE)