metafor (version 1.9-2)

baujat.rma.uni: Baujat Plots for 'rma' Objects

Description

Function to create a Baujat plot for objects of class "rma.uni", "rma.mh", and "rma.peto".

Usage

## S3 method for class 'rma.uni':
baujat(x, xlim, ylim, xlab, ylab, cex, grid=TRUE, \ldots)
## S3 method for class 'rma.mh':
baujat(x, xlim, ylim, xlab, ylab, cex, grid=TRUE, \ldots)
## S3 method for class 'rma.peto':
baujat(x, xlim, ylim, xlab, ylab, cex, grid=TRUE, \ldots)

Arguments

x

an object of class "rma.uni", "rma.mh", or "rma.peto".

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.

grid

logical indicating whether a grid should be added to the plot.

...

other arguments.

Value

• 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.
• Note that the data frame is returned invisibly.

Details

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 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. The points are labeled according to the slab argument (see model fitting functions). If slab was unspecified, then the numbers 1 through $k$ are used by default.

References

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. http://www.jstatsoft.org/v36/i03/.

See Also

rma.uni, rma.mh, rma.peto, influence.rma.uni, radial

Examples

### load data from Pignon et al. (2000)
dat <- get(data(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=id)

### create Baujat plot
baujat(res)