`reitsma`

objects.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.

```
# S3 method for reitsma
print(x, digits = 4, ...)
# S3 method for reitsma
summary(object, level = 0.95, sroc.type = "ruttergatsonis", ...)
# S3 method for reitsma
sroc(fit, fpr = 1:99/100, type = "ruttergatsonis", return_function = FALSE, ...)
# S3 method for reitsma
mcsroc(fit, fpr = 1:99/100, replications = 10000, lambda = 100, ...)
# S3 method for reitsma
ROCellipse(x, level = 0.95, add = FALSE, pch = 1, ...)
# S3 method for reitsma
crosshair(x, level = 0.95, length = 0.1, pch = 1, ...)
# S3 method for 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 reitsma
anova(object, fit2, ...)
# S3 method for anova.reitsma
print(x, digits = 4, ...)
```

x

a `reitsma`

object.

object

a `reitsma`

object.

fit

a `reitsma`

object.

fit2

a `reitsma`

object.

digits

number of decimal digits to print.

level

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

) or regions (`ROCellipse`

)

sroc.type

character, which SROC curve should be used to calculate the AUC in the summary? Besides the default `ruttergatsonis`

the option `naive`

is available.

return_function

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

?

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 `FALSE`

a matrix of points of the ellipse is returned

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

`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.

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`

.

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

```
# NOT RUN {
# 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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