Nothing
## ---- echo = TRUE, fig.width = 5, fig.height = 3-------------------------
library("pimeta")
library("ggplot2")
data(sbp, package = "pimeta")
# a parametric bootstrap prediction interval
piboot <- pima(
y = sbp$y, # effect size estimates
se = sbp$sigmak, # within studies standard errors
B = 25000, # number of bootstrap samples
seed = 14142135, # random number seed
parallel = 4 # multi-threading
)
piboot
plot(piboot, base_size = 10, studylabel = sbp$label)
## ---- echo = TRUE--------------------------------------------------------
# Higgins-Thompson-Spiegelhalter prediction interval
pima(sbp$y, sbp$sigmak, method = "HTS")
## ---- echo = TRUE, fig.width = 5, fig.height = 3-------------------------
m1 <- c(15,12,29,42,14,44,14,29,10,17,38,19,21)
n1 <- c(16,16,34,56,22,54,17,58,14,26,44,29,38)
m2 <- c( 9, 1,18,31, 6,17, 7,23, 3, 6,12,22,19)
n2 <- c(16,16,34,56,22,55,15,58,15,27,45,30,38)
dat <- convert_bin(m1, n1, m2, n2, type = "logOR")
head(dat, n = 3)
pibin <- pima(dat$y, dat$se, seed = 2236067, parallel = 4)
print(pibin, digits = 3, trans = "exp")
binlabel <- c(
"Creytens", "Milo", "Francois and De Nutte", "Deruyttere et al.",
"Hannon", "Roesch", "De Nutte et al.", "Hausken and Bestad",
"Chung", "Van Outryve et al.", "Al-Quorain et al.", "Kellow et al.",
"Yeoh et al.")
plot(pibin, digits = 2, base_size = 10, studylabel = binlabel, trans = "exp")
## ---- eval = FALSE-------------------------------------------------------
# png("forestplot.png", width = 500, height = 300, family = "Arial")
# plot(piboot, digits = 2, base_size = 18, studylabel = sbp$label)
# dev.off()
## ---- eval = FALSE-------------------------------------------------------
# p <- plot(piboot, digits = 2, base_size = 10, studylabel = sbp$label)
# ggsave("forestplot.png", p, width = 5, height = 3, dpi = 150)
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