Draw a drapery plot with (scaled) p-value curves for individual studies and meta-analysis estimates.
drapery(
x,
type = "zvalue",
layout = "grayscale",
study.results = TRUE,
lty.study = 1,
lwd.study = 1,
col.study = "darkgray",
labels,
col.labels = "black",
cex.labels = 0.7,
subset.labels,
srt.labels,
comb.fixed = x$comb.fixed,
comb.random = x$comb.random,
lty.fixed = 1,
lwd.fixed = max(3, lwd.study),
col.fixed = "blue",
lty.random = 1,
lwd.random = lwd.fixed,
col.random = "red",
sign = NULL,
lty.sign = 1,
lwd.sign = 1,
col.sign = "black",
prediction = comb.random,
col.predict = "lightblue",
alpha = if (type == "zvalue") c(0.001, 0.01, 0.05, 0.1) else c(0.01, 0.05, 0.1),
lty.alpha = 2,
lwd.alpha = 1,
col.alpha = "black",
cex.alpha = 0.7,
col.null.effect = "black",
legend = TRUE,
pos.legend = "topleft",
bg = "white",
bty = "o",
backtransf = x$backtransf,
xlab,
ylab,
xlim,
ylim,
lwd.max = 2.5,
lwd.study.weight = if (comb.random) "random" else "fixed",
at = NULL,
n.grid = if (type == "zvalue") 10000 else 1000,
mar = c(5.1, 4.1, 4.1, 4.1),
plot = TRUE,
...
)An object of class meta.
A character string indicating whether to plot test
statistics ("zvalue") or p-values ("pvalue"), can
be abbreviated.
A character string for the line layout of individual
studies: "grayscale", "equal", or
"linewidth" (see Details), can be abbreviated.
A logical indicating whether results for individual studies should be shown in the figure.
Line type for individual studies.
Line width for individual studies.
Colour of lines for individual studies.
A logical or character string indicating whether
study labels should be shown at the top of the drapery plot;
either FALSE, "id", or "studlab"; see
Details.
Colour of study labels.
The magnification for study labels.
A vector specifying which study labels should be shown in the drapery plot.
A numerical vector or single numeric (between 0 and 90) specifying the angle to rotate study labels; see Details.
A logical indicating whether to show result for the fixed effect model.
A logical indicating whether to show result for the random effects model.
Line type for fixed effect meta-analysis.
Line width for fixed effect meta-analysis.
Colour of lines for fixed effect meta-analysis.
Line type for random effects meta-analysis.
Line width for random effects meta-analysis.
Colour of lines for random effects meta-analysis.
Significance level used to highlight significant values in curves.
Line type for significant values.
Line width for significant values.
Line colour for significant values.
A logical indicating whether to show prediction region.
Colour of prediction region
Horizonal lines are printed for the specified alpha values.
Line type of horizonal lines for alpha values.
Line width of horizonal lines for alpha values.
Colour of horizonal lines for alpha values.
The magnification for the text of the alpha
Colour of vertical line indicating null effect.
A logical indicating whether a legend should be printed.
A character string with position of legend (see
legend).
Background colour of legend (see legend).
Type of the box around the legend; either "o" or
"n" (see legend).
A logical indicating whether results should be
back transformed on the x-axis. For example, if backtransf
= FALSE, log odds ratios instead of odds ratios are shown on the
x-axis.
A label for the x-axis.
A label for the y-axis.
The x limits (min, max) of the plot.
The y limits (min, max) of the plot (ignored if
type = "pvalue").
The maximum line width (only considered if argument
layout is equal to "linewidth").
A character string indicating whether to
determine line width for individual studies using weights from
fixed effect ("fixed") or random effects model
("random"), can be abbreviated (only considered if
argument layout is equal to "linewidth").
Points at which tick-marks are to be drawn on the x-axis.
The number of grid points to calculate the p-value or test statistic functions.
Physical plot margin, see par.
A logical indicating whether to generate a figure.
Graphical arguments as in par may also be
passed as arguments.
The concept of a p-value function, also called confidence curve, goes back to Birnbaum (1961). A drapery plot, showing p-value functions (or a scaled version based on the corresponding test statistics) for individual studies as well as meta-analysis estimates, is drawn in the active graphics window. Furthermore, a prediction region for a single future study is shown as a shaded area. In contrast to a forest plot, a drapery plot does not provide information for a single confidence level however for any confidence level.
Argument type can be used to either show p-value functions
(Birnbaum, 1961) or a scaled version (Infanger, 2019) with test
statistics (default).
Argument layout determines how curves for individual studies
are presented:
darker gray tones with increasing precision (layout =
"grayscale")
thicker lines with increasing precision (layout =
"linewidth")
equal lines (layout = "equal")
Argument labels determines how curves of individual studies
are labelled:
number of the study in the (unsorted) forest plot / printout
of a meta-analysis (labels = "id")
study labels provided by argument studlab in
meta-analysis functions (labels = "studlab")
no study labels (labels = FALSE)
By default, study labels are used (labels = "studlab") if no
label has more than three characters; otherwise IDs are used
(labels = "id"). The connection between IDs and study labels
(among other information) is part of a data frame which is
invisibly returned (if argument study.results = TRUE).
Argument srt.labels can be used to change the rotation of
IDs or study labels. By default, study labels are rotated by +/- 45
degrees if at least one study label has more than three characters;
otherwise labels are not rotated.
If labels = "studlab", labels are rotated by -45 degrees for
studies with a treatment estimate below the fixed effect estimate
and otherwise by 45 degrees.
Birnbaum A (1961): Confidence Curves: An Omnibus Technique for Estimation and Testing Statistical Hypotheses. Journal of the American Statistical Association, 56, 246--9
Infanger D and Schmidt-Trucks<U+00E4>ss A (2019): P value functions: An underused method to present research results and to promote quantitative reasoning Statistics in Medicine, 38, 4189--97
# NOT RUN {
data("lungcancer")
m1 <- metainc(d.smokers, py.smokers,
d.nonsmokers, py.nonsmokers,
data = lungcancer, studlab = study)
# Drapery plot
#
drapery(m1, xlim = c(0.5, 50))
# }
# NOT RUN {
data(Fleiss1993bin)
m2 <- metabin(d.asp, n.asp, d.plac, n.plac,
data = Fleiss1993bin, studlab = paste(study, year),
sm = "OR", comb.random = FALSE)
# Produce drapery plot and print data frame with connection between
# IDs and study labels
#
(drapery(m2))
# For studies with a significant effect (p < 0.05), show
# study labels and print labels and lines in red
#
drapery(m2,
labels = "studlab", subset.labels = pval < 0.05,
srt.labels = 0, col.labels = "red",
col.study = ifelse(pval < 0.05, "red", "darkgray"))
# }
# NOT RUN {
# }
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