metafor (version 1.9-4)

forest.rma: Forest Plots (Method for 'rma' Objects)

Description

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

Usage

## S3 method for class 'rma':
forest(x, annotate=TRUE, addfit=TRUE, addcred=FALSE, showweight=FALSE,
       xlim, alim, clim, ylim, at, steps=5,
       level=x$level, digits=2, refline=0, xlab,
       slab, mlab, ilab, ilab.xpos, ilab.pos,
       order, transf, atransf, targs, rows,
       efac=1, pch=15, psize, col, border, lty,
       cex, cex.lab, cex.axis, \dots)

Arguments

x

an object of class "rma".

annotate

logical specifying whether annotations should be added to the plot (default is TRUE).

addfit

logical specifying whether the summary estimate (for models without moderators) or fitted values (for models with moderators) should be added to the plot. See Details.

addcred

logical specifying whether the bounds of the credibility/prediction interval should be added to the plot. See Details.

showweight

logical specifying whether the annotations should also include the weights given to the observed effects or outcomes during the model fitting (default is FALSE).

xlim

horizontal limits of the plot region. If unspecified, the function tries to set the horizontal plot limits to some sensible values.

alim

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

clim

limits for the confidence/credibility/prediction intervals. If unspecified, no limits are used.

ylim

the y 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 (default is 5). Ignored when the user specifies the positions via the at argument.

level

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

digits

integer specifying the number of decimal places to which the tick mark labels of the x-axis and the annotations should be rounded (default is 2). Can also be a vector of two integers, the first specifying the number of decimal places for the annotations,

refline

value at which a vertical reference line should be drawn (default is 0). The line can be suppressed by setting this argument to NA.

xlab

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

slab

optional vector with labels for the $k$ studies. If unspecified, the labels are either taken from the object (if study labels were specified) or simple labels are created within the function. To suppress labels, set this argument to NA.

mlab

optional character string giving a label to the summary estimate from a fixed- or random-effects model. If unspecified, the label is created within the function.

ilab

optional vector or matrix of character strings providing additional information about the studies.

ilab.xpos

vector of numerical value(s) specifying the x-axis position(s) of the character vector(s) given via ilab (must be specified if ilab is specified).

ilab.pos

integer(s) (either 1, 2, 3, or 4) specifying the alignment of the character vector(s) given via ilab (2 means right, 4 mean left aligned). If unspecified, the default is to center the labels.

order

optional character string specifying how the studies should be ordered. See Details.

transf

optional argument specifying the name of a function that should be used to transform the observed effect sizes, summary estimates, fitted values, and confidence interval bounds (e.g., transf=exp). If unspecified, no transformation is used.

atransf

optional argument specifying the name of a function that should be used to transform the x-axis labels and annotations (e.g., transf=exp). If unspecified, no transformation is used.

targs

optional arguments needed by the function specified via transf or atransf.

rows

optional vector specifying the rows (or more generally, the horizontal positions) for plotting the outcomes. If unspecified, the function sets this value automatically. Can also be a single value specifying the row (horizontal position) of the first outco

efac

vertical expansion factor for arrows, confidence interval limits, and the symbol used to denote summary estimates. The default value of 1 should usually work okay.

pch

plotting symbol to use for the observed effect sizes or outcomes. By default, a filled square is used. See points for other options. Can also be a vector of values.

psize

optional vector with point sizes for the observed effect sizes or outcomes. If unspecified, the point sizes are drawn proportional to the precision of the estimates.

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 titel. 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.

col

optional character string specifying the name of a color to use for the summary polygon or fitted values. If unspecified, the function sets a default color.

border

optional character string specifying the name of a color to use for the border of the summary polygon or fitted values. If unspecified, the function sets a default color.

lty

optional character string specifying the line type for the confidence intervals (if unspecified, the function sets this to "solid" by default).

...

other arguments.

Details

The plot shows the individual observed effect sizes or outcomes with corresponding confidence intervals. For fixed- and random-effects models (i.e., for models without moderators), a polygon is added to the bottom of the forest plot, showing the summary estimate based on the model (with the outer edges of the polygon indicating the confidence interval limits). The col and border arguments can be used to adjust the (border) color of the polygon. For random-effects models and if addcred=TRUE, a dotted line indicates the (approximate) bounds of the credibility/prediction interval (the interval indicates where level % of the true effects are expected to fall) (Riley et al., 2011). For random-effects models of class "rma.mv" (see rma.mv) with multiple latex{$\tau^2$}html{$\tau²$}text{$\tau^2$} values, the addcred argument can be used to specify for which level of the inner factor the credibility/prediction intervals should be provided (since the credibility/prediction intervals differ depending on the latex{$\tau^2$}html{$\tau²$}text{$\tau^2$} value). For models involving moderators, the fitted value for each study is added as a polygon to the plot. By default, the width of the polygons corresponds to the confidence interval limits for the fitted values. By setting addcred=TRUE, the width reflects the credibility/prediction interval limits. Again, the col and border arguments can be used to adjust the (border) color of the polygons. These polygons can be suppressed by setting addfit=FALSE. With the transf argument, the observed effect sizes or outcomes, summary estimate, fitted values, confidence interval bounds, and credibility/prediction interval bounds can be transformed with some suitable function. For example, when plotting log odds ratios, one could use transf=exp to obtain a forest plot showing the odds ratios. Alternatively, one can use the atransf argument to transform the x-axis labels and annotations (e.g., atransf=exp). The examples below illustrate the use of these arguments. By default, the studies are ordered from top to bottom (i.e., the first study in the dataset will be placed in row $k$, the second study in row $k-1$, and so on, until the last study, which is placed in the first row). The studies can be reordered with the order argument:

• order="obs": the studies are ordered by the observed effect sizes,
• order="fit": the studies are ordered by the fitted values,
• order="prec": the studies are ordered by their sampling variances,
• order="resid": the studies are ordered by the size of their residuals,
• order="rstandard": the studies are ordered by the size of their standardized residuals,
• order="abs.resid": the studies are ordered by the size of their absolute residuals,
• order="abs.rstandard": the studies are ordered by the size of their absolute standardized residuals.

Alternatively, it is also possible to set order equal to a vector with indices specifying the desired order (see examples below). Additional summary estimates can also be added to the plot with the addpoly function. See the documentation for that function for examples.

References

Lewis, S., & Clarke, M. (2001). Forest plots: Trying to see the wood and the trees. British Medical Journal, 322, 1479--1480. Riley, R. D., Higgins, J. P., & Deeks, J. J. (2011). Interpretation of random effects meta-analyses. British Medical Journal, 342, d549. 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

forest, forest.default, addpoly

Examples

### load BCG vaccine data
data(dat.bcg)

### meta-analysis of the log relative risks using a random-effects model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
           slab=paste(author, year, sep=", "))

### default forest plot of the log relative risks and summary estimate
forest(res)

### summary estimate in row -1; studies in rows k=13 through 1; horizontal
### lines in rows 0 and k+1; and two extra lines of space at the top for
### headings or other annotations
op <- par(xpd=TRUE)
text(x=-8.5, y=-1:16, -1:16, pos=4, cex=.5)
par(op)

### can also inspect defaults chosen
defaults <- forest(res)
defaults

### several forest plots illustrating the use of various arguments
forest(res, cex=.8)
forest(res, cex=.8, addcred=TRUE)
forest(res, cex=.8, alim=c(-3,3))
forest(res, cex=.8, order="prec", alim=c(-3,3))
forest(res, cex=.8, order=order(dat.bcg$ablat), addcred=TRUE)

### adjust xlim values to see how that changes the plot
forest(res)
par("usr")[1:2] ### this shows what xlim values were chosen by default
forest(res, xlim=c(-16,14))
forest(res, xlim=c(-18,10))
forest(res, xlim=c(-10,10))

### illustrate transf argument
forest(res, transf=exp, at=c(0,1,2,4,6), xlim=c(-8,12), cex=.8, refline=1)

### illustrate atransf argument
forest(res, atransf=exp, at=log(c(.05,.25,1,4,20)), xlim=c(-8,7), cex=.8)

### showweight argument
forest(res, atransf=exp, at=log(c(.05,.25,1,4,20)), xlim=c(-8,8),
       order="prec", showweight=TRUE, cex=.8)

### forest plot with extra annotations
forest(res, atransf=exp, at=log(c(.05, .25, 1, 4)), xlim=c(-16,6),
       ilab=cbind(dat.bcg$tpos, dat.bcg$tneg, dat.bcg$cpos, dat.bcg$cneg),
       ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75)
op <- par(cex=.75, font=2)
text(c(-9.5,-8,-6,-4.5), 15, c("TB+", "TB-", "TB+", "TB-"))
text(c(-8.75,-5.25),     16, c("Vaccinated", "Control"))
text(-16,                15, "Author(s) and Year",     pos=4)
text(6,                  15, "Relative Risk [95% CI]", pos=2)
par(op)

### mixed-effects model with absolute latitude in the model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, mods = ~ ablat,
           data=dat.bcg, slab=paste(author, year, sep=", "))

### forest plot with observed and fitted values
forest(res, xlim=c(-9,5), order="fit", cex=.8, ilab=dat.bcg$ablat,
       ilab.xpos=-4, atransf=exp, at=log(c(.05,.25,1,4)))
op <- par(font=2)
text(-9, 15, "Author(s) and Year",    pos=4, cex=.8)
text( 5, 15, "Observed RR [95% CI]",  pos=2, cex=.8)
text(-4, 15, "Latitude",                     cex=.8)
par(op)

### meta-analysis of the log relative risks using a random-effects model
res <- rma(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg,
           slab=paste(author, year, sep=", "))

### for more complicated plots, the ylim and rows arguments may be useful
forest(res)
forest(res, ylim=c(-1.5, 16)) ### the default
forest(res, ylim=c(-1.5, 20)) ### extra space in plot
forest(res, ylim=c(-1.5, 20), rows=c(17:15, 12:6, 3:1)) ### set positions

### forest plot with subgrouping of studies
forest(res, xlim=c(-16, 6), at=log(c(.05, .25, 1, 4)), atransf=exp,
       ilab=cbind(dat.bcg$tpos, dat.bcg$tneg, dat.bcg$cpos, dat.bcg$cneg),
       ilab.xpos=c(-9.5,-8,-6,-4.5), cex=.75, ylim=c(-1, 21),
       order=order(dat.bcg$alloc), rows=c(1:2,5:11,14:17))
op <- par(cex=.75, font=4)
text(-16, c(18,12,3), c("Systematic Allocation", "Random Allocation",
                        "Alternate Allocation"), pos=4)
par(cex=.75, font=2)
text(c(-9.5,-8,-6,-4.5), 20, c("TB+", "TB-", "TB+", "TB-"))
text(c(-8.75,-5.25),     21, c("Vaccinated", "Control"))
text(-16,                20, "Author(s) and Year",     pos=4)
text(6,                  20, "Relative Risk [95% CI]", pos=2)
par(op)

### see also the addpoly.rma function for an example where summaries
### for the three subgroups are added to such a forest plot