phm-class | R Documentation |

`phm`

objects.
Objects of the class `phm`

are output by the function with the same name. Apart from standard methods the function `sroc`

provides SROC curves and confidence bands for model fits.

## S3 method for class 'phm' print(x, ...) ## S3 method for class 'phm' summary(object, level = 0.95, ...) ## S3 method for class 'phm' sroc(fit, fpr = 1:99/100, ...) ## S3 method for class 'phm' plot(x, extrapolate = FALSE, confband = TRUE, level = 0.95, ylim = c(0,1), xlim = c(0,1), sroclty = 1, sroclwd = 1, confbandlty = 2, confbandlwd = 0.5, ...)

`x` |
a |

`object` |
a |

`fit` |
a |

`level` |
numeric, the confidence level for calculations of confidence intervals ( |

`fpr` |
numeric, the false positives rates for which to calculate the predicted sensitivities. |

`extrapolate` |
logical, should the sroc curve be plotted beyond the observed false positive rates? |

`confband` |
logical, should confidence bands be plotted? |

`ylim` |
numeric of length 2, which section of the sensitivities to plot? |

`xlim` |
numeric of length 2, which section of the false positive rates to plot? |

`sroclty` |
integer, line type of the SROC curve |

`sroclwd` |
integer, line width of the SROC curve |

`confbandlty` |
integer, line type of the SROC curve's confidence band |

`confbandlwd` |
integer, line width of the SROC curve's confidence band |

`...` |
arguments to be passed on to other functions |

The SROC curve is derived from the model formula. The confidence bands are calculated from the bounds of the confidence interval for the diagnostic accuracy parameter *θ*. The parameter and its confidence interval are then also used to calculate the AUC and partial AUC using the formulae

*
AUC(a,b) = \int_a^bu^θ\mathrm{d}u = \frac{1}{θ+1}[b^{θ+1}-a^{θ+1}],
*

*
AUC = AUC(0,1)
*

and

*
pAUC = \frac{1}{b-a}AUC(a,b),
*

where *a* is the lower bound of the observed false positive rates and *b* the upper.

The `sroc`

function returns a matrix ready for plotting. Each row corresponds to one point in ROC space.

Philipp Doebler <philipp.doebler@googlemail.com>

Holling, H., Boehning D., Boehning, W. (2012) “Meta-Analysis of Diagnostic Studies based upon SROC-Curves: a Mixed Model Approach using a Proportional Hazards Model.” *Statistical Modelling*, **12**, 347–375.

`phm`

# load data data(AuditC) # fit model fit <- phm(AuditC) #calculate a SROC curve, but do not plot it sroc.AuditC <- sroc(fit) # plot the SROC curve in ROC space as a line plot(sroc.AuditC, type = "l") # Fancy version using plot plot(fit)

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