Description Usage Arguments Value Author(s) References Examples

Functions for making, plotting and analysing ROC curves.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
ROC(TestResult, ...)
## Default S3 method:
ROC(TestResult, D, take.abs = TRUE, ...)
## S3 method for class 'ROC'
plot(x, type = "b", null.line = TRUE,
xlab = "False Pos. Rate", ylab = "True Pos. Rate",
xlim = c(0, 1), ylim = c(0, 1), main = "ROC", ...)
## S3 method for class 'ROC'
points(x, ...)
## S3 method for class 'ROC'
lines(x, ...)
## S3 method for class 'ROC'
identify(x, labels = NULL, ..., digits = 1)
## S3 method for class 'ROC'
print(x, ...)
roc.value(found, true, totalN)
AUC(x, max.mspec)
``` |

`TestResult` |
Typically regression coefficients or t statistics. Note that when p values are used directly, the least significant values would be selected first. In this case one should use 1/p. |

`D` |
True, known, differences, either expressed as a vector of 0
and 1 of the same length as |

`take.abs` |
Logical, indicating whether to take absolute values of the test statistic. |

`x` |
An object of class ROC. |

`type, xlab, ylab, xlim, ylim, main, labels, digits` |
Standard
arguments to functions like |

`null.line` |
Logical, whether to draw the line y = x, corresponding to random guessing. |

`max.mspec` |
Maximal value of the True Positive Rate to consider in AUC calculations. Setting this to a value smaller than one (which is the default) leads to a partial AUC value, which may in many cases be more useful. |

`found` |
The indices of the coefficients identified with a biomarker identification method. |

`true` |
The indices of the true biomarkers. |

`totalN` |
The total number of variables to choose from. |

`...` |
Further arguments, especially useful in the plotting functions. |

Function `ROC`

returns a list with elements:

sensSensitivity, or True Positive Rate (TPR).

mspec1 - Specificity, or False Positive Rate (FPR).

testlevels of the test statistic.

callFunction call.

Function `roc.value`

returns a list with elements `sens`

and
`mspec`

, i.e., one point on a ROC curve.

Function `AUC`

returns the area under the curve, measured up to the
value of `max.mspec`

- if the latter is smaller than 1, it is a
partial AUC curve.

Ron Wehrens

T. Lumley: ROC curves - in Programme's Niche, R News 4/1, June 2004.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
data(spikedApples)
apple.coef <- get.biom(X = spikedApples$dataMatrix,
Y = rep(1:2, each = 10),
fmethod = "vip",
ncomp = 3, type = "coef")
## ROC curve for all VIP values, ordered according to size
true.biom <- (1:ncol(spikedApples$dataMatrix) %in% spikedApples$biom)
vip.roc <- ROC(apple.coef$vip, true.biom)
plot(vip.roc)
## Add stability-based selection point
apple.stab <- get.biom(X = spikedApples$dataMatrix,
Y = rep(1:2, each = 10),
fmethod = "vip",
ncomp = 3, type = "stab")
stab.roc <- roc.value(apple.stab$vip[[1]]$biom.indices,
spikedApples$biom,
totalN = ncol(spikedApples$dataMatrix))
points(stab.roc, col = "red", pch = 19, cex = 1.5)
## Not run:
## Add HC-based selection point
## Attention: takes approx. 2 minutes on my PC
apple.HC <- get.biom(X = spikedApples$dataMatrix,
Y = rep(1:2, each = 10),
fmethod = "vip",
ncomp = 3, type = "HC")
HC.roc <- roc.value(apple.HC$vip$biom.indices,
spikedApples$biom,
totalN = ncol(spikedApples$dataMatrix))
points(HC.roc, col = "blue", pch = 19, cex = 1.5)
## End(Not run)
``` |

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