Baujat Plots for 'rma' Objects
Function to create Baujat plots for objects of class "rma".
baujat(x, ...) ## S3 method for class 'rma' baujat(x, xlim, ylim, xlab, ylab, cex, symbol, grid=TRUE, progbar=FALSE, ...)
x |
an object of class |
xlim |
x-axis limits. If unspecified, the function tries to set the x-axis limits to some sensible values. |
ylim |
y-axis limits. If unspecified, the function tries to set the y-axis limits to some sensible values. |
xlab |
title for the x-axis. If unspecified, the function tries to set an appropriate axis title. |
ylab |
title for the y-axis. If unspecified, the function tries to set an appropriate axis title. |
cex |
optional character expansion factor. If unspecified, the function tries to set this to a sensible value. |
symbol |
either an integer to specify the |
grid |
logical indicating whether a grid should be added to the plot (can also be a color name). |
progbar |
logical indicating whether a progress bar should be shown (the default is |
... |
other arguments. |
Baujat et al. (2002) proposed a diagnostic plot to detect sources of heterogeneity in meta-analytic data. The plot shows the contribution of each study to the overall Q-test statistic for heterogeneity on the x-axis versus the influence of each study (defined as the standardized squared difference between the overall estimate based on a fixed-effects model with and without the study included in the model fitting) on the y-axis. The same type of plot can be produced by first fitting a fixed-effects model with either the rma.uni (using method="FE"), rma.mh, or rma.peto functions and then passing the fitted model object to the baujat function.
For models fitted with the rma.uni function (which may involve moderators and/or which may be random/mixed-effects models), the idea underlying this type of plot can be generalized as follows: The x-axis then corresponds to the squared Pearson residual of a study, while the y-axis corresponds to the standardized squared difference between the predicted/fitted value for the study with and without the study included in the model fitting. Therefore, for a fixed-effect with moderators model, the x-axis corresponds to the contribution of the study to the QE-test statistic for residual heterogeneity.
By default, the points plotted are the study id numbers, but one can also plot the study labels by setting symbol="slab" or one can specify a plotting symbol via the symbol argument that gets passed to pch (see points).
A data frame with components:
x |
the x coordinates of the points that were plotted. |
y |
the y coordinates of the points that were plotted. |
ids |
study id numbers. |
slab |
study labels. |
Note that the data frame is returned invisibly.
Wolfgang Viechtbauer wvb@metafor-project.org http://www.metafor-project.org/
Baujat, B., Mahe, C., Pignon, J.-P., & Hill, C. (2002). A graphical method for exploring heterogeneity in meta-analyses: Application to a meta-analysis of 65 trials. Statistics in Medicine, 21(18), 2641–2652.
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://www.jstatsoft.org/v036/i03.
### copy data from Pignon et al. (2000) into 'dat' dat <- dat.pignon2000 ### compute estimated log hazard ratios and sampling variances dat$yi <- with(dat, OmE/V) dat$vi <- with(dat, 1/V) ### meta-analysis based on all 65 trials res <- rma(yi, vi, data=dat, method="FE", slab=trial) ### create Baujat plot baujat(res) ### some variations of the plotting symbol baujat(res, symbol=19) baujat(res, symbol="slab")
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