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### library(metafor); library(testthat); Sys.setenv(NOT_CRAN="true")
### see also: https://www.metafor-project.org/doku.php/plots:forest_plot_with_subgroups
source("settings.r")
context("Checking plots example: forest plot with subgroups")
test_that("plot can be drawn.", {
expect_equivalent(TRUE, TRUE) # avoid 'Empty test' message
skip_on_cran()
png("images/test_plots_forest_plot_with_subgroups_test.png", res=240, width=1800, height=1800, type="cairo")
### decrease margins so the full space is used
par(mar=c(4,4,1,2))
### copy BCG vaccine meta-analysis data into 'dat'
dat <- dat.bcg
### calculate log risk ratios and corresponding sampling variances (and use
### the 'slab' argument to store study labels as part of the data frame)
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat,
slab=paste(author, year, sep=", "))
### fit random-effects model
res <- rma(yi, vi, data=dat)
### a little helper function to add Q-test, I^2, and tau^2 estimate info
mlabfun <- function(text, x) {
list(bquote(paste(.(text),
" (Q = ", .(fmtx(x$QE, digits=2)),
", df = ", .(x$k - x$p), ", ",
.(fmtp(x$QEp, digits=3, pname="p", add0=TRUE, sep=TRUE, equal=TRUE)), "; ",
I^2, " = ", .(fmtx(x$I2, digits=1)), "%, ",
tau^2, " = ", .(fmtx(x$tau2, digits=2)), ")")))}
### set up forest plot (with 2x2 table counts added; the 'rows' argument is
### used to specify in which rows the outcomes will be plotted)
forest(res, xlim=c(-16, 4.6), at=log(c(0.05, 0.25, 1, 4)), atransf=exp,
ilab=cbind(tpos, tneg, cpos, cneg), ilab.xpos=c(-9.5,-8,-6,-4.5),
cex=0.75, ylim=c(-1, 27), order=alloc, rows=c(3:4,9:15,20:23),
mlab=mlabfun("RE Model for All Studies", res),
psize=1, header="Author(s) and Year")
### set font expansion factor (as in forest() above) and use a bold font
op <- par(cex=0.75, font=2)
### add additional column headings to the plot
text(c(-9.5,-8,-6,-4.5), 26, c("TB+", "TB-", "TB+", "TB-"))
text(c(-8.75,-5.25), 27, c("Vaccinated", "Control"))
### switch to bold italic font
par(font=4)
### add text for the subgroups
text(-16, c(24,16,5), pos=4, c("Systematic Allocation",
"Random Allocation",
"Alternate Allocation"))
### set par back to the original settings
par(op)
### fit random-effects model in the three subgroups
res.s <- rma(yi, vi, subset=(alloc=="systematic"), data=dat)
res.r <- rma(yi, vi, subset=(alloc=="random"), data=dat)
res.a <- rma(yi, vi, subset=(alloc=="alternate"), data=dat)
### add summary polygons for the three subgroups
addpoly(res.s, row=18.5, mlab=mlabfun("RE Model for Subgroup", res.s))
addpoly(res.r, row= 7.5, mlab=mlabfun("RE Model for Subgroup", res.r))
addpoly(res.a, row= 1.5, mlab=mlabfun("RE Model for Subgroup", res.a))
### fit meta-regression model to test for subgroup differences
res <- rma(yi, vi, mods = ~ alloc, data=dat)
### add text for the test of subgroup differences
text(-16, -1.8, pos=4, cex=0.75, bquote(paste("Test for Subgroup Differences: ",
Q[M], " = ", .(fmtx(res$QM, digits=2)),
", df = ", .(res$p - 1), ", ",
.(fmtp(res$QMp, digits=2, pname="p", add0=TRUE, sep=TRUE, equal=TRUE)))))
dev.off()
expect_true(.vistest("images/test_plots_forest_plot_with_subgroups_test.png", "images/test_plots_forest_plot_with_subgroups.png"))
})
rm(list=ls())
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