mada (version 0.5.8)

crosshair: Crosshair plot

Description

Produces a crosshair plot or adds such a plot to an existing plot.

Usage

# S3 method for default
crosshair(x, correction = 0.5, level = 0.95, method = "wilson",
                      xlim = c(0,1), ylim = c(0,1), length = 0.1, pch = 1, 
                      add = FALSE, suppress = TRUE, ...)

Arguments

x

a data frame with variables including TP, FN, FP, TN, alternatively a matrix with column names including these.

correction

numeric, continuity correction applied to zero cells.

level

numeric, confidence level for the calculations of confidence intervals.

method

character, method used to calculate the confidence intervals for sensitivities, specificities and false positive rates. One of "wald", "wilson", "agresti-coull", "jeffreys", "modified wilson", "modified jeffreys", "clopper-pearson", "arcsine", "logit", "witting"

xlim

part of ROC space to be plotted

ylim

part of ROC space to be plotted

length

length of "whiskers" of the crosshair.

pch

Symbol used to plot point estimates. Use pch = "" to suppress plotting point estimates.

add

logical, should the plot be added to the current plot?

suppress

logical, should the warnings produced by the internal call to madad be suppressed? Defaults to TRUE, since only the diagnostic accuracies and their confidence intervals are used in subsequent calculations.

…

further arguments passed on to plot.

Value

Besides plotting, the function returns an invisible NULL.

Details

Crosshair plots go back to Phillips et al. (2010). Note that for fits of the reitsma function a crosshair method is available to plot pooled estimate, see reitsma-class.

References

Phillips, B., Stewart, L.A., & Sutton, A.J. (2010). “'Cross hairs' plots for diagnostic meta-analysis.” Research Synthesis Methods, 1, 308--315.

See Also

ROCellipse, reitsma-class

Examples

# NOT RUN {
data(AuditC)
crosshair(AuditC)
# }