`forest.cumul.rma.Rd`

Function to create forest plots for objects of class `"cumul.rma"`

.

```
# S3 method for cumul.rma
forest(x, annotate=TRUE, header=FALSE,
xlim, alim, olim, ylim, at, steps=5,
level=x$level, refline=0, digits=2L, width,
xlab, ilab, ilab.xpos, ilab.pos,
transf, atransf, targs, rows,
efac=1, pch, psize, col,
lty, fonts, cex, cex.lab, cex.axis, ...)
```

- x
an object of class

`"cumul.rma"`

obtained with`cumul`

.- annotate
logical to specify whether annotations should be added to the plot (the default is

`TRUE`

).- header
logical to specify whether column headings should be added to the plot (the default is

`FALSE`

). Can also be a character vector to specify the left and right headings (or only the left one).- xlim
horizontal limits of the plot region. If unspecified, the function tries to set the horizontal plot limits to some sensible values.

- alim
the x-axis limits. If unspecified, the function tries to set the x-axis limits to some sensible values.

- olim
optional argument to specify observation/outcome limits. If unspecified, no limits are used.

- ylim
the y-axis limits of the plot. If unspecified, the function tries to set the y-axis limits to some sensible values.

- at
position of the x-axis tick marks and corresponding labels. If unspecified, the function tries to set the tick mark positions/labels to some sensible values.

- steps
the number of tick marks for the x-axis (the default is 5). Ignored when the positions are specified via the

`at`

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

- refline
numeric value to specify the location of the vertical ‘reference’ line (the default is 0). The line can be suppressed by setting this argument to

`NA`

.- digits
integer to specify the number of decimal places to which the tick mark labels of the x-axis and the annotations should be rounded (the default is

`2L`

). Can also be a vector of two integers, the first to specify the number of decimal places for the annotations, the second for the x-axis labels. When specifying an integer (e.g.,`2L`

), trailing zeros after the decimal mark are dropped for the x-axis labels. When specifying a numeric value (e.g.,`2`

), trailing zeros are retained.- width
optional integer to manually adjust the width of the columns for the annotations (either a single integer or a vector of the same length as the number of annotation columns).

- xlab
title for the x-axis. If unspecified, the function tries to set an appropriate axis title.

- ilab
optional vector, matrix, or data frame providing additional information about the studies that should be added to the plot.

- ilab.xpos
numeric vector to specify the horizontal position(s) of the variable(s) given via

`ilab`

(must be specified if`ilab`

is specified).- ilab.pos
integer(s) (either 1, 2, 3, or 4) to specify the alignment of the vector(s) given via

`ilab`

(2 means right, 4 mean left aligned). If unspecified, the default is to center the labels.- transf
optional argument to specify a function to transform the estimates and confidence interval bounds (e.g.,

`transf=exp`

; see also transf). If unspecified, no transformation is used.- atransf
optional argument to specify a function to transform the x-axis labels and annotations (e.g.,

`atransf=exp`

; see also transf). If unspecified, no transformation is used.- targs
optional arguments needed by the function specified via

`transf`

or`atransf`

.- rows
optional vector to specify the rows (or more generally, the horizontal positions) for plotting the outcomes. Can also be a single value to specify the row (horizontal position) of the first outcome (the remaining outcomes are then plotted below this starting row). If unspecified, the function sets this value automatically.

- efac
vertical expansion factor for confidence interval limits and arrows. The default value of 1 should usually work okay. Can also be a vector of two numbers, the first for CI limits, the second for arrows.

- pch
plotting symbol to use for the estimates. By default, a filled square is used. See

`points`

for other options. Can also be a vector of values.- psize
numeric value to specify the point sizes for the estimates (the default is 1). Can also be a vector of values.

- col
optional character string to specify the color to use for plotting (

`"black"`

is used by default if not specified). Can also be a vector.- lty
optional character string to specify the line type for the confidence intervals. If unspecified, the function sets this to

`"solid"`

by default.- fonts
optional character string to specify the font to use for the study labels, annotations, and the extra information (if specified via

`ilab`

). If unspecified, the default font is used.- cex
optional character and symbol expansion factor. If unspecified, the function tries to set this to a sensible value.

- cex.lab
optional expansion factor for the x-axis title. If unspecified, the function tries to set this to a sensible value.

- cex.axis
optional expansion factor for the x-axis labels. If unspecified, the function tries to set this to a sensible value.

- ...
other arguments.

The plot shows the estimated (average) outcome with corresponding confidence interval as one study at a time is added to the analysis.

The function tries to set some sensible values for the optional arguments, but it may be necessary to adjust these in certain circumstances.

The function actually returns some information about the chosen values invisibly. Printing this information is useful as a starting point to make adjustments to the plot.

If the number of studies is quite large, the labels, annotations, and symbols may become quite small and impossible to read. Stretching the plot window vertically may then provide a more readable figure (one should call the function again after adjusting the window size, so that the label/symbol sizes can be properly adjusted). Also, the `cex`

, `cex.lab`

, and `cex.axis`

arguments are then useful to adjust the symbol and text sizes.

If the horizontal plot and/or x-axis limits are set manually, then the horizontal plot limits (`xlim`

) must be at least as wide as the x-axis limits (`alim`

). This restriction is enforced inside the function.

If the outcome measure used for creating the plot is bounded (e.g., correlations are bounded between -1 and +1, proportions are bounded between 0 and 1), one can use the `olim`

argument to enforce those limits (the observed outcomes and confidence intervals cannot exceed those bounds then).

The `lty`

argument can also be a vector of two elements, the first for specifying the line type of the individual CIs (`"solid"`

by default), the second for the line type of the horizontal line that is automatically added to the plot (`"solid"`

by default; set to `"blank"`

to remove it).

Chalmers, T. C., & Lau, J. (1993). Meta-analytic stimulus for changes in clinical trials. *Statistical Methods in Medical Research*, **2**(2), 161–172. https://doi.org/10.1177/096228029300200204

Lau, J., Schmid, C. H., & Chalmers, T. C. (1995). Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care. *Journal of Clinical Epidemiology*, **48**(1), 45–57. https://doi.org/10.1016/0895-4356(94)00106-z

Lewis, S., & Clarke, M. (2001). Forest plots: Trying to see the wood and the trees. *British Medical Journal*, **322**(7300), 1479–1480. https://doi.org/10.1136/bmj.322.7300.1479

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

```
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
### fit random-effects model
res <- rma(yi, vi, data=dat, slab=paste(author, year, sep=", "))
### draw cumulative forest plots
x <- cumul(res, order=year)
forest(x, cex=.8, header=TRUE)
forest(x, alim=c(-2,1), cex=.8, header=TRUE)
### 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=", "))
### draw cumulative forest plot
x <- cumul(res, order=year)
forest(x, alim=c(-2,1), cex=.8, header=TRUE)
```