Function to add a polygon to a forest plot showing the summary estimate with corresponding confidence interval based on an object of class "rma".

# S3 method for rma
addpoly(x, row=-2, level=x\$level, annotate,
addpred=FALSE, digits, width, mlab, transf, atransf, targs,
efac, col, border, lty, fonts, cex, ...)

## Arguments

x

an object of class "rma".

row

numeric value to specify the row (or more generally, the horizontal position) for plotting the polygon (the default is -2).

level

numeric value between 0 and 100 to specify the confidence interval level (the default is to take the value from the object).

annotate

optional logical to specify whether annotations for the summary estimate should be added to the plot.

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 string giving a label for the summary estimate polygon. If unspecified, the function sets a default label.

transf

optional argument to specify a function to transform the summary estimate and confidence interval bound (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 polygon.

col

optional character string to specify color to use for the polygon. If unspecified, the function sets a default color.

border

optional character string to specify the color to use for the border of the polygon. 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 label and annotations.

cex

optional symbol expansion factor.

...

other arguments.

## Details

The function can be used to add a four-sided polygon, sometimes called a summary ‘diamond’, to an existing forest plot created with the forest function. The polygon shows the summary estimate (with its confidence interval bounds) based on an equal- or a random-effects model. Using this function, summary estimates based on different types of models can be shown in the same plot. Also, summary estimates based on a subgrouping of the studies can be added to the plot this way. See ‘Examples’.

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

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

### meta-analysis of the log risk ratios using the Mantel-Haenszel method
res <- rma.mh(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 summary estimate
forest(res, atransf=exp, xlim=c(-8,6), ylim=c(-2.5,16), header=TRUE)

### meta-analysis of the log risk ratios using a random-effects model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)

### add summary estimate from the random-effects model to the forest plot

### forest plot with subgrouping of studies and summaries per subgroup
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
slab=paste(author, year, sep=", "))
forest(res, xlim=c(-16, 4.6), at=log(c(.05, .25, 1, 4)), atransf=exp,
ilab=cbind(tpos, tneg, cpos, cneg), ilab.xpos=c(-9.5,-8,-6,-4.5),
cex=.75, ylim=c(-1, 27), order=alloc, rows=c(3:4,9:15,20:23),
mlab="RE Model for All Studies", header="Author(s) and Year")
op <- par(cex=.75, font=2)
text(c(-9.5,-8,-6,-4.5), 26, c("TB+", "TB-", "TB+", "TB-"))
text(c(-8.75,-5.25),     27, c("Vaccinated", "Control"))
par(font=4)
text(-16, c(24,16,5), c("Systematic Allocation", "Random Allocation",
"Alternate Allocation"), pos=4)
par(op)
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
subset=(alloc=="systematic"))
addpoly(res, row=18.5, mlab="RE Model for Subgroup")
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
subset=(alloc=="random"))
addpoly(res, row=7.5, mlab="RE Model for Subgroup")
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
subset=(alloc=="alternate"))
addpoly(res, row=1.5, mlab="RE Model for Subgroup")