plot.diagmeta {diagmeta}  R Documentation 
Provides several plots for metaanalysis of diagnostic test accuracy studies with the multiple cutoffs model
## S3 method for class 'diagmeta' plot( x, which = c("survival", "youden", "roc", "sroc"), xlab = "Threshold", main, ci = FALSE, ciSens = FALSE, ciSpec = FALSE, mark.optcut = FALSE, mark.cutpoints = FALSE, points = TRUE, lines = FALSE, rlines = TRUE, line.optcut = TRUE, col.points = "rainbow", cex = 1, pch.points = 16, col = "black", col.ci = "gray", col.optcut = "black", cex.marks = 0.7 * cex, lwd = 1, lwd.ci = lwd, lwd.optcut = 2 * lwd, lwd.study = lwd, shading = "none", col.hatching = col.ci, lwd.hatching = lwd.ci, ellipse = FALSE, xlim = NULL, ... )
x 
An object of class 
which 
A character vector indicating the type of plot, either

xlab 
An x axis label 
main 
A logical indicating title to the plot 
ci 
A logical indicating whether confidence intervals should
be plotted for 
ciSens 
A logical indicating whether confidence intervals
should be plotted for sensitivity, given the specificity in

ciSpec 
A logical indicating whether confidence intervals
should be plotted for specificity, given the sensitivity in

mark.optcut 
A logical indicating whether the optimal cutoff
should be marked on 
mark.cutpoints 
A logical indicating whether the given
cutoffs should be marked on 
points 
A logical indicating whether points should be plotted
in plots 
lines 
A logical indicating whether polygonal lines
connecting points belonging to the same study should be printed
in plots 
rlines 
A logical indicating whether regression lines or
curves should be plotted for plots 
line.optcut 
A logical indicating whether a vertical line
should be plotted at the optimal cutoff line for plots

col.points 
A character string indicating color of points,
either 
cex 
A numeric indicating magnification to be used for plotting text and symbols 
pch.points 
A numeric indicating plot symbol(s) for points 
col 
A character string indicating color of lines 
col.ci 
A character string indicating color of confidence lines 
col.optcut 
A character string indicating color of optimal cutoff line 
cex.marks 
A numeric indicating magnification(s) to be used for marking cutoffs 
lwd 
A numeric indicating line width 
lwd.ci 
A numeric indicating line width of confidence lines 
lwd.optcut 
A numeric indicating line width of optimal cutoff 
lwd.study 
A numeric indicating line width of individual studies 
shading 
A character indicating shading and hatching
confidence region in 
col.hatching 
A character string indicating color used in hatching of the confidence region 
lwd.hatching 
A numeric indicating line width used in hatching of the confidence region 
ellipse 
A logical indicating whether a confidence ellipse should be drawn around the optimal cutoff 
xlim 
A character or numerical vector indicating the minimum and maximum value for the horizontal axes 
... 
Additional graphical arguments 
The first argument of the plot function is an object of class "diagmeta".
The second argument which
indicates which sort of plot(s)
should be shown. For which="regression"
, a scatter plot of
the quantiltransformed proportions of negative test results with
two regression lines is shown. Points belonging to the same study
are marked with the same colour. For which="cdf"
, the two
cumulative distribution functions are shown, corresponding to the
proportions of negative test results. For which="survival"
,
the survival functions are shown, corresponding to the proportions
of positive test results. For which="Youden"
, the
(potentially weighted) sum of sensitivity and specificity minus 1
is shown; in case of lambda=0.5
(the default) this is the
Youden index. For which="ROC"
, studyspecific ROC curves are
shown. For which="SROC"
, the modelbased summary ROC curve
is shown. For which="density"
, the modelbased densities of
both groups are shown. For which="sensspec"
, the sensitivity
(decreasing with increasing cutoff) and the specificity (increasing
with increasing cutoff) are shown. Instead of character strings, a
numeric value or vector can be used to specify plots with numbers
corresponding to the following order of plots: "regression", "cdf",
"survival", "youden", "roc", "sroc", "density", and "sensspec".
Other arguments refer to further plot parameters, such as
lines
(whether points belonging to the same study should be
joined), rlines
(whether regression curves should be drawn),
ci
/ ciSens
/ ciSpec
/ ellipse
(whether
confidence regions should be shown), line.optcut
/
mark.optcut
(whether the optimal cutoff should be
indicated), and additional plot parameters (see Arguments).
If no further arguments are provided, four standard plots ("survival", "Youden", "ROC", and "SROC") are produced in a 2 x 2 format.
Gerta Rücker ruecker@imbi.unifreiburg.de, Susanne Steinhauser susanne.steinhauser@unikoeln.de, Srinath Kolampally kolampal@imbi.unifreiburg.de, Guido Schwarzer sc@imbi.unifreiburg.de
Schneider A, Linde K, Reitsma JB, Steinhauser S, Rücker G (2017): A novel statistical model for analyzing data of a systematic review generates optimal cutoff values for fractional exhaled nitric oxide for asthma diagnosis. Journal of Clinical Epidemiology, 92, 69–78
Steinhauser S, Schumacher M, Rücker G (2016): Modelling multiple thresholds in metaanalysis of diagnostic test accuracy studies. BMC Medical Research Methodology, 16, 97
# FENO dataset # data(Schneider2017) diag1 < diagmeta(tpos, fpos, tneg, fneg, cutpoint, studlab = paste(author, year, group), data = Schneider2017, log.cutoff = TRUE) # Regression plot with confidence intervals # plot(diag1, which = "regr", lines = FALSE, ci = TRUE) # Cumulative distribution plot with optimal cutoff line and # confidence intervals # plot(diag1, which = "cdf", line.optcut = TRUE, ci = TRUE) # Survival plot with optimal cutoff line and confidence intervals # plot(diag1, which = "survival", line.optcut = TRUE, ci = TRUE) # Youden plot of optimal cutoff line and confidence intervals # plot(diag1, which = "youden", lines = TRUE, line.optcut = TRUE, ci = TRUE) # ROC plot of lines connecting points belonging to the same study # plot(diag1, which = "ROC", lines = TRUE) # SROC plot of confidence regions for sensitivity and specificity # with optimal cutoff mark # plot(diag1, which = "SROC", ciSens = TRUE, ciSpec = TRUE, mark.optcut = TRUE, shading = "hatch") # Density plot of densities for both groups with optimal cutoff # line # plot(diag1, which = "density", line.optcut = TRUE)