`baujat.Rd`

Function to create Baujat plots for objects of class `"rma"`

.

baujat(x, ...) # S3 method for rma baujat(x, xlim, ylim, xlab, ylab, cex, symbol="ids", 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 to specify whether a grid should be added to the plot (can also be a color name). |

progbar | logical to specify whether a progress bar should be shown (the default is |

... | other arguments. |

The model specified via `x`

must be a model fitted with either the `rma.uni`

, `rma.mh`

, or `rma.peto`

functions.

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 (Viechtbauer, 2021): 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-effects with moderators model, the x-axis corresponds to the contribution of the study to the \(Q_E\)-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"`

(if study labels are available within the model object) or one can specify a plotting symbol via the `symbol`

argument that gets passed to `pch`

(see `points`

for possible options).

A data frame with components:

the x-axis coordinates of the points that were plotted.

the y-axis coordinates of the points that were plotted.

the study id numbers.

the study labels.

Wolfgang Viechtbauer wvb@metafor-project.org https://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. https://doi.org/10.1002/sim.1221

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. *Journal of Statistical Software*, **36**(3), 1--48. https://doi.org/10.18637/jss.v036.i03

Viechtbauer, W. (2021). Model checking in meta-analysis. In C. H. Schmid, T. Stijnen, & I. R. White (Eds.), *Handbook of meta-analysis* (pp. 219--254). Boca Raton, FL: CRC Press. https://doi.org/10.1201/9781315119403

### copy data from Pignon et al. (2000) into 'dat' dat <- dat.pignon2000 ### calculate 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")