# Methods for reitsma objects.

### Description

Objects of the class `reitsma`

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

, `mcsroc`

and `ROCellipse`

provide SROC curves and confidence regions for fits.

### Usage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## S3 method for class 'reitsma'
print(x, digits = 4, ...)
## S3 method for class 'reitsma'
summary(object, level = 0.95, sroc.type = "ruttergatsonis", ...)
## S3 method for class 'reitsma'
sroc(fit, fpr = 1:99/100, type = "ruttergatsonis", return_function = FALSE, ...)
## S3 method for class 'reitsma'
mcsroc(fit, fpr = 1:99/100, replications = 10000, lambda = 100, ...)
## S3 method for class 'reitsma'
ROCellipse(x, level = 0.95, add = FALSE, pch = 1, ...)
## S3 method for class 'reitsma'
crosshair(x, level = 0.95, length = 0.1, pch = 1, ...)
## S3 method for class 'reitsma'
plot(x, extrapolate = FALSE, plotsumm = TRUE, level = 0.95,
ylim = c(0,1), xlim = c(0,1), pch = 1, sroclty = 1, sroclwd = 1,
predict = FALSE, predlty = 3, predlwd = 1, type = "ruttergatsonis", ...)
## S3 method for class 'reitsma'
anova(object, fit2, ...)
## S3 method for class 'anova.reitsma'
print(x, digits = 4, ...)
``` |

### Arguments

`x` |
a |

`object` |
a |

`fit` |
a |

`fit2` |
a |

`digits` |
number of decimal digits to print. |

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

`sroc.type` |
character, which SROC curve should be used to calculate the AUC in the summary? Besides the default |

`return_function` |
logical. Should a function on ROC space be returned or the values at the points given by |

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

`replications` |
integer, the number of replications for the Monte-Carlo SROC curve |

`lambda` |
numeric, the parameter lambda of the Monte-Carlo run, see details |

`add` |
logical, should the confidence region be added to the current plot? If set to |

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

`plotsumm` |
logical, should the summary pair of sensitivity and false positive rate together with its confidence region be plotted? |

`length` |
positve numeric, length of the "whiskers" of the crosshairs. |

`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? |

`pch` |
integer, symbol for the pair of mean sensitivity and false positive rate |

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

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

`predict` |
logical, draw prediction region? |

`predlty` |
integer, line type of prediction region |

`predlwd` |
integer, line width of prediction region |

`type` |
character, type of SROC curve to plot. Can be either the generalization of the Rutter & Gatsonis (2001) SROC curve (see below) or the naive curve implied the bivariate model. |

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

### Details

The confidence regions of `ROCellipse`

are first calculated as ellipses on logit-ROC space, so the back-transformed regions that are output are not necessarily ellipses. The Monte-Carlo SROC curves are generated from random samples from the fitted model and a `lowess`

smooth through them is output. Many computational details are to be found in Doebler et al. (2012).

The `summary`

function for `reitsma`

objects also contains the five parameters of the HSROC model by Rutter & Gatsonis (2001) if no regression is performed. These values are calculated by using the formulae from Harbord et al. (2007).

The `plot`

method for `reitsma`

objects will plot the generalization of the Rutter-Gatsonis curve.

If you require positive or negative likelihood ratios, you should use `SummaryPts`

.

### Value

`sroc`

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

returns a `lowess`

smooth. `ROCellipse`

returns a list, the first element being a matrix of points in ROC space that delimit the confidence region and the second is the point estimate of the pair of sensitivity and false positive rate in ROC space.

### Author(s)

Philipp Doebler <philipp.doebler@googlemail.com>

### References

Doebler, P., Holling, H., Boehning, D. (2012) “A Mixed Model Approach to Meta-Analysis of Diagnostic Studies with Binary Test Outcome.” *Psychological Methods*, to appear

### See Also

`reitsma`

, `SummaryPts`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# load data
data(Dementia)
# fit model
fit <- reitsma(Dementia)
# calculate a confidence region but do not plot it
cr.Dementia <- ROCellipse(fit)
#calculate a SROC curve
sroc.Dementia <- sroc(fit)
# plot the confidence region in ROC space as a line
plot(cr.Dementia$ROCellipse, type = "l", xlim = c(0,1), ylim = c(0,1))
# add the point estimate of the mean
points(cr.Dementia$fprsens)
# add the SROC curve
lines(sroc.Dementia)
``` |

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