# addpoly.predict.rma: Add Polygons to Forest Plots (Method for 'predict.rma'... In metafor: Meta-Analysis Package for R

## Add Polygons to Forest Plots (Method for 'predict.rma' Objects)

### Description

Function to add one or more polygons to a forest plot based on an object of class "predict.rma".

### Usage

## S3 method for class 'predict.rma'
addpred=FALSE, digits, width, mlab, transf, atransf, targs,
efac, col, border, lty, fonts, cex, ...)


### Arguments

 x an object of class "predict.rma". rows vector to specify the rows (or more generally, the horizontal positions) for plotting the polygons (defaults is -2). Can also be a single value to specify the row (horizontal position) of the first polygon (the remaining polygons are then plotted below this starting row). annotate optional logical to specify whether annotations should be added to the plot for the polygons that are drawn. addpred logical to specify whether the bounds of the prediction interval should be added to the plot (the default is FALSE). digits optional integer to specify the number of decimal places to which the annotations should be rounded. width optional integer to manually adjust the width of the columns for the annotations. mlab optional character vector with the same length as x giving labels for the polygons that are drawn. transf optional argument to specify a function to transform the x values and confidence interval bounds (e.g., transf=exp; see also transf). atransf optional argument to specify a function to transform the annotations (e.g., atransf=exp; see also transf). targs optional arguments needed by the function specified via transf or atransf. efac optional vertical expansion factor for the polygons. col optional character string to specify the color to use for the polygons. If unspecified, the function sets a default color. border optional character string to specify color to use for the border of the polygons. If unspecified, the function sets a default color. lty optional character string to specify the line type for the prediction interval. If unspecified, the function sets this to "dotted" by default. fonts optional character string to specify the font to use for the labels and annotations. cex optional symbol expansion factor. ... other arguments.

### Details

The function can be used to add one or more polygons to an existing forest plot created with the forest function. For example, summary estimates based on a model involving moderators can be added to the plot this way (see ‘Examples’).

To use the function, one should specify the values at which the polygons should be drawn (via the x argument) together with the corresponding variances (via the vi argument) or with the corresponding standard errors (via the sei argument). Alternatively, one can specify the values at which the polygons should be drawn together with the corresponding confidence interval bounds (via the ci.lb and ci.ub arguments). Optionally, one can also specify the bounds of the corresponding prediction interval bounds via the pi.lb and pi.ub arguments.

If unspecified, arguments annotate, digits, width, transf, atransf, targs, efac (only if the forest plot was created with forest.rma), fonts, cex, annosym, and textpos are automatically set equal to the same values that were used when creating the forest plot.

### Author(s)

Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org

### References

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

forest.rma and forest.default for functions to draw forest plots to which polygons can be added.

### Examples

### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg,
data=dat.bcg, slab=paste(author, year, sep=", "))

### forest plot of the observed risk ratios
with(dat, forest(yi, vi, atransf=exp, xlim=c(-8,4), ylim=c(-4.5,16),
at=log(c(.05, .25, 1, 4)), cex=.8, order=alloc,

### fit mixed-effects model with allocation method as a moderator
res <- rma(yi, vi, mods = ~ 0 + alloc, data=dat)

### predicted log risk ratios for the different allocation methods
x <- predict(res, newmods=diag(3))

### add predicted average risk ratios to forest plot