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Commit 0d3e9e62 authored by SamCH93's avatar SamCH93
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Make command to extract code, last polish of MS

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data/preprocess-rpcb-data.R
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......@@ -156,7 +156,7 @@ datClean2 %>%
geom_hline(yintercept = 0, lty = 2, alpha = 0.9, linewidth = 0.4) +
geom_vline(xintercept = 0, lty = 2, alpha = 0.9, linewidth = 0.4) +
geom_point(aes(fill = rsig), shape = 21, size = 2) +
geom_rug(aes(color = rsig), size = 0.3, show.legend = FALSE) +
geom_rug(aes(color = rsig), linewidth = 0.3, show.legend = FALSE) +
labs(x = "Original effect estimate (SMD)",
y = "Replication effect estimate (SMD)",
fill = "") +
......
paper/Makefile
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......@@ -23,6 +23,10 @@ pdf2:
pdflatex $(FILE)
pdflatex $(FILE)
## extract the R code from Rnw file
code:
Rscript -e "knitr::purl('$(FILE).Rnw')" --vanilla
## clean
clean:
-rm $(FILE).aux $(FILE).blg $(FILE).log $(FILE).tex $(FILE).bbl \
......
paper/rsabsence.R 0 → 100755
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## ----"setup", include = FALSE-------------------------------------------------
## knitr options
library(knitr)
opts_chunk$set(fig.height = 4,
echo = FALSE,
warning = FALSE,
message = FALSE,
cache = FALSE,
eval = TRUE)
## should sessionInfo be printed at the end?
Reproducibility <- TRUE
## packages
library(ggplot2) # plotting
library(gridExtra) # combining ggplots
library(dplyr) # data manipulation
library(reporttools) # reporting of p-values
## not show scientific notation for small numbers
options("scipen" = 10)
## the replication Bayes factor under normality
BFr <- function(to, tr, so, sr) {
bf <- dnorm(x = tr, mean = 0, sd = so) /
dnorm(x = tr, mean = to, sd = sqrt(so^2 + sr^2))
return(bf)
}
## function to format Bayes factors
formatBF. <- function(BF) {
if (is.na(BF)) {
BFform <- NA
} else if (BF > 1) {
if (BF > 1000) {
BFform <- "> 1000"
} else {
BFform <- as.character(signif(BF, 2))
}
} else {
if (BF < 1/1000) {
BFform <- "< 1/1000"
} else {
BFform <- paste0("1/", signif(1/BF, 2))
}
}
if (!is.na(BFform) && BFform == "1/1") {
return("1")
} else {
return(BFform)
}
}
formatBF <- Vectorize(FUN = formatBF.)
## Bayes factor under normality with unit-information prior under alternative
BF01 <- function(estimate, se, null = 0, unitvar = 4) {
bf <- dnorm(x = estimate, mean = null, sd = se) /
dnorm(x = estimate, mean = null, sd = sqrt(se^2 + unitvar))
return(bf)
}
## ----"data"-------------------------------------------------------------------
## data
rpcbRaw <- read.csv(file = "../data/rpcb-effect-level.csv")
rpcb <- rpcbRaw %>%
mutate(
## recompute one-sided p-values based on normality
## (in direction of original effect estimate)
zo = smdo/so,
zr = smdr/sr,
po1 = pnorm(q = abs(zo), lower.tail = FALSE),
pr1 = pnorm(q = abs(zr), lower.tail = ifelse(sign(zo) < 0, TRUE, FALSE)),
## compute some other quantities
c = so^2/sr^2, # variance ratio
d = smdr/smdo, # relative effect size
po2 = 2*(1 - pnorm(q = abs(zo))), # two-sided original p-value
pr2 = 2*(1 - pnorm(q = abs(zr))), # two-sided replication p-value
sm = 1/sqrt(1/so^2 + 1/sr^2), # standard error of fixed effect estimate
smdm = (smdo/so^2 + smdr/sr^2)*sm^2, # fixed effect estimate
pm2 = 2*(1 - pnorm(q = abs(smdm/sm))), # two-sided fixed effect p-value
Q = (smdo - smdr)^2/(so^2 + sr^2), # Q-statistic
pQ = pchisq(q = Q, df = 1, lower.tail = FALSE), # p-value from Q-test
BForig = BF01(estimate = smdo, se = so), # unit-information BF for original
BForigformat = formatBF(BF = BForig),
BFrep = BF01(estimate = smdr, se = sr), # unit-information BF for replication
BFrepformat = formatBF(BF = BFrep)
)
rpcbNull <- rpcb %>%
filter(resulto == "Null")
## 2 examples
study1 <- "(20, 1, 1)" # evidence of absence
study2 <- "(29, 2, 2)" # absence of evidence
plotDF1 <- rpcbNull %>%
filter(id %in% c(study1, study2)) %>%
mutate(label = ifelse(id == study1,
"Goetz et al. (2011)\nEvidence of absence",
"Dawson et al. (2011)\nAbsence of evidence"))
conflevel <- 0.95
## ----"2-example-studies", fig.height = 3--------------------------------------
## create plot showing two example study pairs with null results
ggplot(data = plotDF1) +
facet_wrap(~ label) +
geom_hline(yintercept = 0, lty = 2, alpha = 0.3) +
geom_pointrange(aes(x = "Original", y = smdo,
ymin = smdo - qnorm(p = (1 + conflevel)/2)*so,
ymax = smdo + qnorm(p = (1 + conflevel)/2)*so), fatten = 3) +
geom_pointrange(aes(x = "Replication", y = smdr,
ymin = smdr - qnorm(p = (1 + conflevel)/2)*sr,
ymax = smdr + qnorm(p = (1 + conflevel)/2)*sr), fatten = 3) +
geom_text(aes(x = 1.05, y = 2.5,
label = paste("italic(n) ==", no)), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
geom_text(aes(x = 2.05, y = 2.5,
label = paste("italic(n) ==", nr)), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
geom_text(aes(x = 1.05, y = 3,
label = paste("italic(p) ==", formatPval(po))), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
geom_text(aes(x = 2.05, y = 3,
label = paste("italic(p) ==", formatPval(pr))), col = "darkblue",
parse = TRUE, size = 3.8, hjust = 0) +
labs(x = "", y = "Standardized mean difference") +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
strip.text = element_text(size = 12, margin = margin(4), vjust = 1.5),
strip.background = element_rect(fill = alpha("tan", 0.4)),
axis.text = element_text(size = 12))
## ----"plot-null-findings-rpcb", fig.height = 8.25, fig.width = "0.95\\linewidth"----
## compute TOST p-values
## Wellek (2010): strict - 0.36 # liberal - .74
# Cohen: small - 0.3 # medium - 0.5 # large - 0.8
## 80-125% convention for AUC and Cmax FDA/EMA
## 1.3 for oncology OR/HR -> log(1.3)*sqrt(3)/pi = 0.1446
margin <- 0.74
conflevel <- 0.9
rpcbNull$ptosto <- with(rpcbNull, pmax(pnorm(q = smdo, mean = margin, sd = so,
lower.tail = TRUE),
pnorm(q = smdo, mean = -margin, sd = so,
lower.tail = FALSE)))
rpcbNull$ptostr <- with(rpcbNull, pmax(pnorm(q = smdr, mean = margin, sd = sr,
lower.tail = TRUE),
pnorm(q = smdr, mean = -margin, sd = sr,
lower.tail = FALSE)))
## highlight the studies from Goetz and Dawson
ex1 <- "(20, 1, 1)"
ind1 <- which(rpcbNull$id == ex1)
ex2 <- "(29, 2, 2)"
ind2 <- which(rpcbNull$id == ex2)
rpcbNull$id <- ifelse(rpcbNull$id == ex1,
"(20, 1, 1) - Goetz et al. (2011)", rpcbNull$id)
rpcbNull$id <- ifelse(rpcbNull$id == ex2,
"(29, 2, 2) - Dawson et al. (2011)", rpcbNull$id)
## create plots of all study pairs with null results in original study
ggplot(data = rpcbNull) +
## order in ascending original paper order and label with id variable
facet_wrap(~ paper + experiment + effect + id,
labeller = label_bquote(.(id)), scales = "free", ncol = 3) +
geom_hline(yintercept = 0, lty = 2, alpha = 0.25) +
## equivalence margin
geom_hline(yintercept = c(-margin, margin), lty = 3, col = 2, alpha = 0.9) +
## ## also show the 95% CIs
## geom_linerange(aes(x = "Original", y = smdo,
## ymin = smdo - qnorm(p = (1 + 0.95)/2)*so,
## ymax = smdo + qnorm(p = (1 + 0.95)/2)*so), size = 0.2, alpha = 0.6) +
## geom_linerange(aes(x = "Replication", y = smdr,
## ymin = smdr - qnorm(p = (1 + 0.95)/2)*sr,
## ymax = smdr + qnorm(p = (1 + 0.95)/2)*sr), size = 0.2, alpha = 0.6) +
## 90% CIs
geom_pointrange(aes(x = "Original", y = smdo,
ymin = smdo - qnorm(p = (1 + conflevel)/2)*so,
ymax = smdo + qnorm(p = (1 + conflevel)/2)*so),
size = 0.5, fatten = 1.5) +
geom_pointrange(aes(x = "Replication", y = smdr,
ymin = smdr - qnorm(p = (1 + conflevel)/2)*sr,
ymax = smdr + qnorm(p = (1 + conflevel)/2)*sr),
size = 0.5, fatten = 1.5) +
annotate(geom = "ribbon", x = seq(0, 3, 0.01), ymin = -margin, ymax = margin,
alpha = 0.05, fill = 2) +
labs(x = "", y = "Standardized mean difference") +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(n) ==", no)), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0, vjust = 2) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(n) ==", nr)), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0, vjust = 2) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)",
ifelse(po < 0.0001, "", "=="),
formatPval(po))), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)",
ifelse(pr < 0.0001, "", "=="),
formatPval(pr))), col = "darkblue",
parse = TRUE, size = 2.3, hjust = 0) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)['TOST']",
ifelse(ptosto < 0.0001, "", "=="),
formatPval(ptosto))),
col = "darkblue", parse = TRUE, size = 2.3, hjust = 0, vjust = 3) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("italic(p)['TOST']",
ifelse(ptostr < 0.0001, "", "=="),
formatPval(ptostr))),
col = "darkblue", parse = TRUE, size = 2.3, hjust = 0, vjust = 3) +
geom_text(aes(x = 1.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("BF['01']", ifelse(BForig <= 1/1000, "", "=="),
BForigformat)), col = "darkblue", parse = TRUE,
size = 2.3, hjust = 0, vjust = 4.5) +
geom_text(aes(x = 2.05, y = pmax(smdo + 2.5*so, smdr + 2.5*sr, 1.1*margin),
label = paste("BF['01']", ifelse(BFrep <= 1/1000, "", "=="),
BFrepformat)), col = "darkblue", parse = TRUE,
size = 2.3, hjust = 0, vjust = 4.5) +
coord_cartesian(x = c(1.1, 2.4)) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.text = element_text(size = 8, margin = margin(3), vjust = 2),
strip.background = element_rect(fill = alpha("tan", 0.4)),
axis.text = element_text(size = 8))
## ----"successes-RPCB"---------------------------------------------------------
ntotal <- nrow(rpcbNull)
## successes non-significance criterion
nullSuccesses <- sum(rpcbNull$po > 0.05 & rpcbNull$pr > 0.05)
## success equivalence testing criterion
equivalenceSuccesses <- sum(rpcbNull$ptosto <= 0.05 & rpcbNull$ptostr <= 0.05)
ptosto1 <- rpcbNull$ptosto[ind1]
ptostr1 <- rpcbNull$ptostr[ind1]
ptosto2 <- rpcbNull$ptosto[ind2]
ptostr2 <- rpcbNull$ptostr[ind2]
## success BF criterion
bfSuccesses <- sum(rpcbNull$BForig > 3 & rpcbNull$BFrep > 3)
BForig1 <- rpcbNull$BForig[ind1]
BFrep1 <- rpcbNull$BFrep[ind1]
BForig2 <- rpcbNull$BForig[ind2]
BFrep2 <- rpcbNull$BFrep[ind2]
## ----"sensitivity", fig.height = 6.5------------------------------------------
## compute number of successful replications as a function of the equivalence margin
marginseq <- seq(0.01, 4.5, 0.01)
alphaseq <- c(0.01, 0.05, 0.1)
sensitivityGrid <- expand.grid(m = marginseq, a = alphaseq)
equivalenceDF <- lapply(X = seq(1, nrow(sensitivityGrid)), FUN = function(i) {
m <- sensitivityGrid$m[i]
a <- sensitivityGrid$a[i]
rpcbNull$ptosto <- with(rpcbNull, pmax(pnorm(q = smdo, mean = m, sd = so,
lower.tail = TRUE),
pnorm(q = smdo, mean = -m, sd = so,
lower.tail = FALSE)))
rpcbNull$ptostr <- with(rpcbNull, pmax(pnorm(q = smdr, mean = m, sd = sr,
lower.tail = TRUE),
pnorm(q = smdr, mean = -m, sd = sr,
lower.tail = FALSE)))
successes <- sum(rpcbNull$ptosto <= a & rpcbNull$ptostr <= a)
data.frame(margin = m, alpha = a,
successes = successes, proportion = successes/nrow(rpcbNull))
}) %>%
bind_rows()
## plot number of successes as a function of margin
nmax <- nrow(rpcbNull)
bks <- c(0, 3, 6, 9, 11, 15)
labs <- paste0(bks, " (", round(bks/nmax*100, 0), "%)")
rpcbSuccesses <- 11
marbks <- c(0, margin, 1, 2, 3, 4)
plotA <- ggplot(data = equivalenceDF,
aes(x = margin, y = successes,
color = factor(alpha, ordered = TRUE, levels = rev(alphaseq)))) +
facet_wrap(~ 'italic("p")["TOST"] <= alpha ~ "in original and replication study"',
labeller = label_parsed) +
geom_vline(xintercept = margin, lty = 3, alpha = 0.4) +
annotate(geom = "segment", x = margin + 0.25, xend = margin + 0.01, y = 2, yend = 2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = margin + 0.28, y = 2, color = "darkgrey",
label = "margin used in main analysis",
size = 3, alpha = 0.9, hjust = 0) +
geom_hline(yintercept = rpcbSuccesses, lty = 2, alpha = 0.4) +
annotate(geom = "segment", x = 0.1, xend = 0.1, y = 13, yend = 11.2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = -0.04, y = 13.5, color = "darkgrey",
label = "non-significance criterion successes",
size = 3, alpha = 0.9, hjust = 0) +
geom_step(alpha = 0.8, linewidth = 0.8) +
scale_y_continuous(breaks = bks, labels = labs) +
scale_x_continuous(breaks = marbks) +
coord_cartesian(xlim = c(0, max(equivalenceDF$margin))) +
labs(x = bquote("Equivalence margin" ~ Delta),
y = "Successful replications",
color = bquote("threshold" ~ alpha)) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.background = element_rect(fill = alpha("tan", 0.4)),
strip.text = element_text(size = 12),
legend.position = c(0.85, 0.25),
plot.background = element_rect(fill = "transparent", color = NA),
legend.box.background = element_rect(fill = "transparent", colour = NA))
## compute number of successful replications as a function of the prior scale
priorsdseq <- seq(0, 40, 0.1)
bfThreshseq <- c(3, 6, 10)
sensitivityGrid2 <- expand.grid(s = priorsdseq, thresh = bfThreshseq)
bfDF <- lapply(X = seq(1, nrow(sensitivityGrid2)), FUN = function(i) {
priorsd <- sensitivityGrid2$s[i]
thresh <- sensitivityGrid2$thresh[i]
rpcbNull$BForig <- with(rpcbNull, BF01(estimate = smdo, se = so, unitvar = priorsd^2))
rpcbNull$BFrep <- with(rpcbNull, BF01(estimate = smdr, se = sr, unitvar = priorsd^2))
successes <- sum(rpcbNull$BForig >= thresh & rpcbNull$BFrep >= thresh)
data.frame(priorsd = priorsd, thresh = thresh,
successes = successes, proportion = successes/nrow(rpcbNull))
}) %>%
bind_rows()
## plot number of successes as a function of prior sd
priorbks <- c(0, 2, 10, 20, 30, 40)
plotB <- ggplot(data = bfDF,
aes(x = priorsd, y = successes, color = factor(thresh, ordered = TRUE))) +
facet_wrap(~ '"BF"["01"] >= gamma ~ "in original and replication study"',
labeller = label_parsed) +
geom_vline(xintercept = 2, lty = 3, alpha = 0.4) +
geom_hline(yintercept = rpcbSuccesses, lty = 2, alpha = 0.4) +
annotate(geom = "segment", x = 7, xend = 2 + 0.2, y = 0.5, yend = 0.5,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = 7.5, y = 0.5, color = "darkgrey",
label = "standard deviation used in main analysis",
size = 3, alpha = 0.9, hjust = 0) +
annotate(geom = "segment", x = 0.5, xend = 0.5, y = 13, yend = 11.2,
arrow = arrow(type = "closed", length = unit(0.02, "npc")), alpha = 0.9,
color = "darkgrey") +
annotate(geom = "text", x = 0.05, y = 13.5, color = "darkgrey",
label = "non-significance criterion successes",
size = 3, alpha = 0.9, hjust = 0) +
geom_step(alpha = 0.8, linewidth = 0.8) +
scale_y_continuous(breaks = bks, labels = labs, limits = c(0, nmax)) +
scale_x_continuous(breaks = priorbks) +
coord_cartesian(xlim = c(0, max(bfDF$priorsd))) +
labs(x = "Prior standard deviation",
y = "Successful replications ",
color = bquote("threshold" ~ gamma)) +
theme_bw() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
strip.background = element_rect(fill = alpha("tan", 0.4)),
strip.text = element_text(size = 12),
legend.position = c(0.85, 0.25),
plot.background = element_rect(fill = "transparent", color = NA),
legend.box.background = element_rect(fill = "transparent", colour = NA))
grid.arrange(plotA, plotB, ncol = 1)
## ----"interesting-study"------------------------------------------------------
studyInteresting <- filter(rpcbNull, id == "(48, 2, 4)")
noInteresting <- studyInteresting$no
nrInteresting <- studyInteresting$nr
## ----"sessionInfo2", echo = Reproducibility, results = Reproducibility--------
cat(paste(Sys.time(), Sys.timezone(), "\n"))
sessionInfo()
paper/rsabsence.Rnw
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......@@ -247,42 +247,14 @@ rpcb <- rpcbRaw %>%
rpcbNull <- rpcb %>%
filter(resulto == "Null")
## check the sample sizes
## paper 5 (https://osf.io/q96yj) - 1 Cohen's d - sample size correspond to forest plot
## paper 9 (https://osf.io/yhq4n) - 3 Cohen's w- sample size do not correspond at all
## paper 15 (https://osf.io/ytrx5) - 1 r - sample size correspond to forest plot
## paper 19 (https://osf.io/465r3) - 2 Cohen's dz - sample size correspond to forest plot
## paper 20 (https://osf.io/acg8s) - 1 r and 1 Cliff's delta - sample size correspond to forest plot
## paper 21 (https://osf.io/ycq5g) - 1 Cohen's d - sample size correspond to forest plot
## paper 24 (https://osf.io/pcuhs) - 2 Cohen's d - sample size correspond to forest plot
## paper 28 (https://osf.io/gb7sr/) - 3 Cohen's d - sample size correspond to forest plot
## paper 29 (https://osf.io/8acw4) - 1 Cohen's d - sample size do not correspond, seem to be double
## paper 41 (https://osf.io/qnpxv) - 1 Hazard ratio - sample size correspond to forest plot
## paper 47 (https://osf.io/jhp8z) - 2 r - sample size correspond to forest plot
## paper 48 (https://osf.io/zewrd) - 1 r - sample size do not correspond to forest plot for original study
## 2 examples
## some evidence for absence of effect https://doi.org/10.7554/eLife.45120 I
## can't find the replication effect like reported in the data set :( let's take
## it at face value we are not data detectives
## https://iiif.elifesciences.org/lax/45120%2Felife-45120-fig4-v1.tif/full/1500,/0/default.jpg
study1 <- "(20, 1, 1)"
## absence of evidence
study2 <- "(29, 2, 2)"
## https://iiif.elifesciences.org/lax/25306%2Felife-25306-fig5-v2.tif/full/1500,/0/default.jpg
study1 <- "(20, 1, 1)" # evidence of absence
study2 <- "(29, 2, 2)" # absence of evidence
plotDF1 <- rpcbNull %>%
filter(id %in% c(study1, study2)) %>%
mutate(label = ifelse(id == study1,
"Goetz et al. (2011)\nEvidence of absence",
"Dawson et al. (2011)\nAbsence of evidence"))
## ## RH: this data is really a mess. turns out for Dawson n represents the group
## ## size (n = 6 in https://osf.io/8acw4) while in Goetz it is the sample size of
## ## the whole experiment (n = 34 and 61 in https://osf.io/acg8s). in study 2 the
## ## so multiply by 2 to have the total sample size, see Figure 5A
## ## https://doi.org/10.7554/eLife.25306.012
## plotDF1$no[plotDF1$id == study2] <- plotDF1$no[plotDF1$id == study2]*2
## plotDF1$nr[plotDF1$id == study2] <- plotDF1$nr[plotDF1$id == study2]*2
conflevel <- 0.95
@
......@@ -627,9 +599,6 @@ effect estimates even lie outside the equivalence margin.
% We chose the margin $\Delta = \Sexpr{margin}$ primarily for illustrative
% purposes and because effect sizes in preclinical research are typically much
% larger than in clinical research.
The post-hoc specification of equivalence margins is controversial. Ideally, the
margin should be specified on a case-by-case basis in a pre-registered protocol
before the studies are conducted by researchers familiar with the subject
......@@ -656,7 +625,7 @@ replications as a function of the margin $\Delta$ and for different TOST
proposed by \citet{Hauck1986}. We see that for realistic margins between $0$ and
$1$, the proportion of replication successes remains below $50\%$ for the
conventional $\alpha = 0.05$ level. To achieve a success rate of
$11/15 = \Sexpr{round(11/15*100, 1)}\%$, as was achieved with the
$11/15 = \Sexpr{round(11/15*100, 0)}\%$, as was achieved with the
non-significance criterion from the RPCB, unrealistic margins of $\Delta > 2$
are required, highlighting the paucity of evidence provided by these studies.
Changing the success criterion to a more lenient level ($\alpha = 0.1$) or a
......@@ -892,18 +861,18 @@ deviations larger than the unit-information standard deviation of $2$, as this
corresponds to the assumption of very large effect sizes under the alternatives.
However, to achieve replication success for a larger proportion of replications
than the observed
$\Sexpr{bfSuccesses}/\Sexpr{ntotal} = \Sexpr{round(bfSuccesses/ntotal*100, 1)}\%$,
$\Sexpr{bfSuccesses}/\Sexpr{ntotal} = \Sexpr{round(bfSuccesses/ntotal*100, 0)}\%$,
unreasonably large prior standard deviations have to be specified. For instance,
a standard deviation of roughly $5$ is required to achieve replication success
in $50\%$ of the replications at a lenient Bayes factor threshold of
$\gamma = 3$. The standard deviation needs to be almost $20$ so that the same
success rate $11/15 = \Sexpr{round(11/15*100, 1)}\%$ as with the
success rate $11/15 = \Sexpr{round(11/15*100, 0)}\%$ as with the
non-significance criterion is achieved. The necessary standard deviations are
even higher for stricter Bayes factor threshold, such as $\gamma = 6$ or
$\gamma = 10$.
<< >>=
<< "interesting-study" >>=
studyInteresting <- filter(rpcbNull, id == "(48, 2, 4)")
noInteresting <- studyInteresting$no
nrInteresting <- studyInteresting$nr
......
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