baujat.meta  R Documentation 
Draw a Baujat plot to explore heterogeneity in metaanalysis.
## S3 method for class 'meta'
baujat(
x,
yscale = 1,
xlim,
ylim,
xlab = "Contribution to overall heterogeneity",
ylab = "Influence on overall result",
pch = 21,
cex = 1,
col = "black",
bg = "darkgray",
studlab = TRUE,
cex.studlab = 0.8,
pos.studlab,
offset = 0.5,
xmin = 0,
ymin = 0,
grid = TRUE,
col.grid = "lightgray",
lty.grid = "dotted",
lwd.grid = par("lwd"),
pty = "s",
pooled,
...
)
x 
An object of class 
yscale 
Scaling factor for values on yaxis. 
xlim 
The x limits (min,max) of the plot. 
ylim 
The y limits (min,max) of the plot. 
xlab 
A label for the xaxis. 
ylab 
A label for the yaxis. 
pch 
The plotting symbol used for individual studies. 
cex 
The magnification to be used for plotting symbol. 
col 
A vector with colour of plotting symbols. 
bg 
A vector with background colour of plotting symbols (only
used if 
studlab 
A logical indicating whether study labels should be
printed in the graph. A vector with study labels can also be
provided (must be of same length as 
cex.studlab 
The magnification for study labels. 
pos.studlab 
Position of study labels, see argument

offset 
Offset for study labels (see 
xmin 
A numeric specifying minimal value to print study labels (on xaxis). 
ymin 
A numeric specifying minimal value to print study labels (on yaxis). 
grid 
A logical indicating whether a grid is printed in the plot. 
col.grid 
Colour for grid lines. 
lty.grid 
The line type for grid lines. 
lwd.grid 
The line width for grid lines. 
pty 
A character specifying type of plot region (see

pooled 
A character string indicating whether a common effect
or random effects model is used for pooling. Either missing (see
Details), 
... 
Graphical arguments as in 
Baujat et al. (2002) introduced a scatter plot to explore
heterogeneity in metaanalysis. On the xaxis the contribution of
each study to the overall heterogeneity statistic (see list object
Q
of the metaanalysis object x
) is plotted. On the
yaxis the standardised difference of the overall treatment effect
with and without each study is plotted; this quantity describes the
influence of each study on the overal treatment effect.
Information from object x
is utilised if argument
pooled
is missing. A common effect model is assumed
(pooled="common"
) if argument x$common
is
TRUE
; a random effects model is assumed
(pooled="random"
) if argument x$random
is
TRUE
and x$common
is FALSE
.
Internally, the metainf
function is used to calculate
the values on the yaxis.
A data.frame with the following variables:
x 
Coordinate on xaxis (contribution to heterogeneity statistic) 
y 
Coordinate on yaxis (influence on overall treatment effect) 
Guido Schwarzer guido.schwarzer@uniklinikfreiburg.de
Baujat B, Mahé C, Pignon JP, Hill C (2002): A graphical method for exploring heterogeneity in metaanalyses: Application to a metaanalysis of 65 trials. Statistics in Medicine, 30, 2641–52
metagen
, metainf
data(Olkin1995)
# Only consider first ten studies
m1 < metabin(ev.exp, n.exp, ev.cont, n.cont,
data = Olkin1995, sm = "OR", method = "I", studlab = paste(author, year),
subset = 1:10)
# Generate Baujat plot
baujat(m1)
## Not run:
m1 < metabin(ev.exp, n.exp, ev.cont, n.cont,
data = Olkin1995, sm = "OR", method = "I", studlab = paste(author, year))
# Do not print study labels if the xvalue is smaller than 4 and
# the yvalue is smaller than 1
baujat(m1, yscale = 10, xmin = 4, ymin = 1)
# Change position of study labels
baujat(m1, yscale = 10, xmin = 4, ymin = 1,
pos = 1, xlim = c(0, 6.5))
# Generate Baujat plot and assign x and y coordinates to R object
# b1
b1 < baujat(m1)
# Calculate overall heterogeneity statistic
sum(b1$x)
m1$Q
## End(Not run)
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