Nothing
library(bootES)
library(compiler)
## Constants
kFinal <- list(resultCols=c('stat', 'ci.low', 'ci.upper', 'bias', 'se'))
## Compiler Options
enableJIT(3)
. <- setCompilerOptions('optimize'=3)
## Functions to run the simulation
newResultMatrix <- function(reps) {
cols <- c('stat', 'ci.low', 'ci.upper', 'bias', 'se')
matrix(NA_real_, nrow=reps, ncol=length(cols), dimnames=list(NULL, cols))
}
doBootES <- function(R=1000, n, mean, sd) {
dat <- data.frame(x=rnorm(n, mean, sd))
res <- summary(bootES(dat, R, data.col="x"))
as.vector(res)
}
doBootES <- cmpfun(doBootES)
## Parameters for the Monte Carlo Simulation
u1 <- 5
s1 <- 1
n <- 50
## Simulation 1
## Repetitions: 10,000
## Sample Size: 50
## bootES Samples: 1000
set.seed(1)
reps <- 1e4
dat <- data.frame(x=rnorm(n * reps, u1, s1))
mc.sim.1 <- sapply(seq_len(reps), function(x) {
cat(x, "\n")
idx <- (n * (x - 1) + 1):(n * (x - 1) + n)
res <- summary(bootES(dat[idx, ,drop=FALSE], R=1000, data.col="x"))
as.vector(res)
})
mc.sim.1 <- t(mc.sim.1)
colnames(mc.sim.1) <- kFinal$resultCols
cvg <- mc.sim.1[, 'ci.low'] < u1 & u1 < mc.sim.1[, 'ci.upper']
sum(cvg, na.rm=T)
## Simulation 2
## Repetitions: 10,000
## Sample Size: 50
## bootES Samples: 999
set.seed(2)
reps <- 1e4
mc.sim.2 <- newResultMatrix(reps)
for (i in seq_len(reps)) {
cat(i, "\n")
mc.sim.2[i, ] <- doBootES(R=999, n, u1, s1)
}
cvg <- mc.sim.2[, 'ci.low'] < u1 & u1 < mc.sim.2[, 'ci.upper']
sum(cvg, na.rm=T)
## Simulation 3
## Repetitions: 10,000
## Sample Size: 50
## bootES Samples: 1999
set.seed(2)
reps <- 1e4
mc.sim.3 <- newResultMatrix(reps)
for (i in seq_len(reps)) {
cat(i, "\n")
mc.sim.3[i, ] <- doBootES(R=1999, n, u1, s1)
}
cvg <- mc.sim.3[, 'ci.low'] < u1 & u1 < mc.sim.3[, 'ci.upper']
sum(cvg, na.rm=T)
## Simulation 3
## Repetitions: 10,000
## Sample Size: 50
## bootES Samples: 1999
set.seed(2)
u1 <- 5
s1 <- 1
reps <- 10000
mc.sim.4 <- newResultMatrix(reps)
for (i in seq_len(reps)) {
cat(i, "\n")
mc.sim.4[i, ] <- doBootES(R=1999, n=100, u1, s1)
}
cvg <- mc.sim.4[, 'ci.low'] < u1 & u1 < mc.sim.4[, 'ci.upper']
sum(cvg, na.rm=T)
mc.out <- "bootES.mc.RData"
if (!file.exists("bootES.mc.RData"))
save(mc.sim.1, mc.sim.2, mc.sim.3, mc.sim.4, file="bootES.mc.RData",
compress="xz")
## Formula for 'r' from 'd'
## r <- d / (sqrt(d^2 + 4))
## Simulation 4
## Repetitions: 10,000
## Sample Size: 50 x 50
## bootES Samples: 999
## Simulation Parameters
reps <- 1e4
## Population Parameters
# TODO
# u1 <- 5; s1 <- 1
# u2 <- 7; s2 <- 1
# cohens.d.sigma <- (u1 - u2) / s1
# r.pop <- cohens.d.sigma / sqrt(cohens.d.sigma^2 + 4)
#
# set.seed(1)
# dat <- data.frame(x=c(rnorm(50, u1, s1), rnorm(50, u2, s2)))
# dat$group <- rep(c('A', 'B'), each=50)
# mc.sim.4 <- newResultMatrix(reps)
# for (i in seq_len(reps)) {
# cat(i, "\n")
# mc.sim.4[i, ] <- doBootES(dat, R=999, 'x', 'group')
# }
# cvg <- mc.sim.4[, 'ci.low'] < u1 & u1 < mc.sim.4[, 'ci.upper']
# sum(cvg, na.rm=T)
# mc.sim.1 <- t(mc.sim.1)
# colnames(mc.sim.1) <- kFinal$resultCols
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.