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Commit 68f3e9d5 authored by Boulakis Paradeisios Alexandros's avatar Boulakis Paradeisios Alexandros
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??pwr
sesoi.eff <- pwr.t.test(n=previous.sample,
sig.level=alpha.level,
power = 1/3,
type="two.sample",
alternative="two.sided" )
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwr)
# Find SESOI -------------------------------------------------------------------
previous.sample <- 25
alpha.level <- .01
beta.level <- .05
# find the SESOI
# ask for help using ??pwr
sesoi.eff <- pwr.t.test(n=previous.sample,
sig.level=alpha.level,
power = 1/3,
type="two.sample",
alternative="two.sided" )
sesoi.eff
sesoi.eff$n
pwr.t.test(sig.level=alpha.level,
power = beta.level,
d=sesoi.eff$d
type="two.sample",
pwr.t.test(sig.level=alpha.level,
power = beta.level,
d=sesoi.eff$d,
type="two.sample",
alternative="two.sided" )
pwr.t.test(sig.level=alpha.level,
power = 1- beta.level,
d=sesoi.eff$d,
type="two.sample",
alternative="two.sided" )
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
numeric(len(sample.sizes))
storage.power <- numeric(length(sample.sizes))
storage.power
?rnorm
?t.test
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (sample.size in sample.sizes){
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
control.data <- rnorm(n=sample.size,mean = 0, sd= sd)
smoker.data <- rnorm(n=sample.size,mean = cohen.d, sd= sd)
results <- t.test(control.data,smoker.data)
}
}
results
control.data
smoker.data
results$statistic
results$p.value
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.size,mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.size,mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
storage.pvals
storage.pvals
storage.pvals < alpha.error
mean(storage.pvals < alpha.error)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes)){
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
storage.power
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(ii,sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
sample.sizes
sample.sizes[ii]
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 2
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
storage.power
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 1
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
storage.power
# Packages ---------------------------------------------------------------------
list.of.packages <- c("ggplot2")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(ggplot2)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 1
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
) +
theme_minimal()
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_vline(xintercept=1-beta.error)
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(xintercept=1-beta.error)
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error)
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", size = 1)
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", linewidth = 1) +
labs(
title = "Simulation for Power Analysis",
x = "Sample Size",
y = "Achieved Power",
)
target.sample <- sample.sizes[storage.power > 1-beta.error][1]
target.sample
# Packages ---------------------------------------------------------------------
list.of.packages <- c("ggplot2")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(ggplot2)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 500
cohen.d <- .6
sd <- 1
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
# print when your results was above your threshold
target.sample <- sample.sizes[storage.power > 1-beta.error][1]
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", linewidth = 1) +
labs(
title = sprintf("Simulation for Power Analysis: Required sample size = %s",target.sample),
x = "Sample Size",
y = "Achieved Power",
)
# Packages ---------------------------------------------------------------------
list.of.packages <- c("ggplot2")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(ggplot2)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <- 5000
cohen.d <- .6
sd <- 1
alpha.error <- .01
beta.error <- .05
sample.sizes <- seq(10,200,5)
# Simulation ------------------------------------------------------------------
storage.power <- numeric(length(sample.sizes))
#for every sample size
for (ii in seq(length(sample.sizes))){
print(sample.sizes[ii])
# create a storage vector for pvals
storage.statistic <- numeric(n.sim)
# create a storage vector for statistic
storage.pvals <- numeric(n.sim)
# for every simulation
for (simulation in seq(n.sim)){
# simulate random normally distributed samples for group 1 and add noise
control.data <- rnorm(n=sample.sizes[ii],mean = 0, sd= sd)
# simulate random normally distributed samples for group 2, add your effect and noise
smoker.data <- rnorm(n=sample.sizes[ii],mean = cohen.d, sd= sd)
# do an independent t-test
results <- t.test(control.data,smoker.data)
# store statistic and pvals
storage.statistic[simulation] <- results$statistic
storage.pvals[simulation] <- results$p.value
}
# count how many results where significant
achieved.power <- mean(storage.pvals < alpha.error)
storage.power[ii] <- achieved.power
}
# print when your results was above your threshold
target.sample <- sample.sizes[storage.power > 1-beta.error][1]
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", linewidth = 1) +
labs(
title = sprintf("Simulation for Power Analysis: Required sample size = %s",target.sample),
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", linewidth = 1) +
geom_hline(xintercept=target.sample,color = 'red', linetype = "dashed", linewidth = 1) +
labs(
title = sprintf("Simulation for Power Analysis: Required sample size = %s",target.sample),
x = "Sample Size",
y = "Achieved Power",
)
ggplot(data=NULL,aes(x=sample.sizes,y=storage.power))+
geom_line()+
geom_point()+
geom_hline(yintercept=1-beta.error,color = 'red', linetype = "dashed", linewidth = 1) +
geom_vline(xintercept=target.sample,color = 'red', linetype = "dashed", linewidth = 1) +
labs(
title = sprintf("Simulation for Power Analysis: Required sample size = %s",target.sample),
x = "Sample Size",
y = "Achieved Power",
)
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwr","rpact")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwr)
library(rpact)
??pwr
??pwr
sesoi.eff <- pwr.t.test(n=previous.sample.cont + previous.sample.depr,
sig.level=alpha.level,
power = 1/3,
type="two.sample",
alternative="two.sided" )
previous.sample.cont <- 50
previous.sample.depr <- 44
alpha.error <- .01
beta.error <- .05
sesoi.eff <- pwr.t.test(n=previous.sample.cont + previous.sample.depr,
sig.level=alpha.level,
power = 1/3,
type="two.sample",
alternative="two.sided" )
sesoi.eff
sesoi.eff <- pwr.t.test(n=50,
sig.level=alpha.level,
power = 1/3,
type="two.sample",
alternative="two.sided" )
sesoi.eff
power <- pwr.t.test(sig.level=alpha.level,
power = 1- beta.level,
d=sesoi.eff$d,
type="two.sample",
alternative="two.sided" )
power
??rpact
design <- getDesignGroupSequential(
kMax = 4,
typeOfDesign = "P",
sided = 2,
alpha = alpha.error,
beta = beta.error
)
print(summary(design))
?getDesignGroupSequential
plot_eff_against_power.R → eff_per_power/plot_eff_against_power.R
+ 67
− 67
View file @ 68f3e9d5
library(ggplot2)
# make results replicable
set.seed(2024)
# specify how many simulations you want to run
nSimul <- 10000
# specify how many effects size you want to test
effects <- seq(.1,.9,.1)
# specify how many sample sizes you want to test
samples <- seq(10,150,10)
# Storage for plotting
results <- list()
# Simulation loop
for (effect in effects) {
sample.storage <- numeric(length(samples))
for (ii in seq_along(samples)) {
p.storage <- numeric(nSimul)
for (simulation in seq(nSimul)) {
# run statistical test
x <- rnorm(n = samples[ii], mean = 0, sd = 1)
y <- rnorm(n = samples[ii], mean = effect, sd = 1)
#extract significance
z <- t.test(x, y)
p.storage[simulation] <- z$p.value
}
achieved.power <- mean(p.storage < 0.05)
sample.storage[ii] <- achieved.power
}
# Store results for the current effect size
results[[paste0("effect_", effect)]] <- data.frame(
sample_size = samples,
achieved_power = sample.storage
)
}
# Combine results into a single data frame for plotting
plot_data <- do.call(rbind, lapply(names(results), function(effect) {
data <- results[[effect]]
data$effect <- effect
data
}))
# Convert effect to numeric for better plotting
plot_data$effect <- as.numeric(gsub("effect_", "", plot_data$effect))
# Create the plot
ggplot(plot_data, aes(x = sample_size, y = achieved_power, color = factor(effect))) +
geom_line(linewidth = 1) +
labs(
title = "Achieved Power vs. Sample Size",
x = "Sample Size",
y = "Achieved Power",
color = "Effect Size"
) +
theme_minimal()
library(ggplot2)
# make results replicable
set.seed(2024)
# specify how many simulations you want to run
nSimul <- 10000
# specify how many effects size you want to test
effects <- seq(.1,.9,.1)
# specify how many sample sizes you want to test
samples <- seq(10,150,10)
# Storage for plotting
results <- list()
# Simulation loop
for (effect in effects) {
sample.storage <- numeric(length(samples))
for (ii in seq_along(samples)) {
p.storage <- numeric(nSimul)
for (simulation in seq(nSimul)) {
# run statistical test
x <- rnorm(n = samples[ii], mean = 0, sd = 1)
y <- rnorm(n = samples[ii], mean = effect, sd = 1)
#extract significance
z <- t.test(x, y)
p.storage[simulation] <- z$p.value
}
achieved.power <- mean(p.storage < 0.05)
sample.storage[ii] <- achieved.power
}
# Store results for the current effect size
results[[paste0("effect_", effect)]] <- data.frame(
sample_size = samples,
achieved_power = sample.storage
)
}
# Combine results into a single data frame for plotting
plot_data <- do.call(rbind, lapply(names(results), function(effect) {
data <- results[[effect]]
data$effect <- effect
data
}))
# Convert effect to numeric for better plotting
plot_data$effect <- as.numeric(gsub("effect_", "", plot_data$effect))
# Create the plot
ggplot(plot_data, aes(x = sample_size, y = achieved_power, color = factor(effect))) +
geom_line(linewidth = 1) +
labs(
title = "Achieved Power vs. Sample Size",
x = "Sample Size",
y = "Achieved Power",
color = "Effect Size"
) +
theme_minimal()
power_per_effect.png → eff_per_power/power_per_effect.png
+ 0
− 0
View file @ 68f3e9d5
File moved
ex_01_params.R → ex01/ex_01_params.R
+ 55
− 55
View file @ 68f3e9d5
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwrss")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwrss)
# Independent t-test -----------------------------------------------------------
# parameters
cont.mean <- 130
drug.mean <- 120
cont.sd <- 15
drug.sd <- 15
alpha.error <- .05
beta.error <- .05
alternative <- "not equal" # also try "greater" or "less"
# find the correct function
# assign the correct parameters
# you can look around the documentation of pwrss library using ??pwrss
t.test.power <-
plot(t.test.power)
# Paired t-test ----------------------------------------------------------------
# parameters
before.mean <- 130
after.mean <- 120
before.sd <- 15
after.sd <- 15
alpha.error <- .05
beta.error <- .05
alternative <- "not equal" # also try "greater" or "less"
# find the correct function
# assign the correct parameters
# you can look around the documentation of pwrss library using ??pwrss
paired.t.power <-
plot(paired.t.power)
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwrss")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwrss)
# Independent t-test -----------------------------------------------------------
# parameters
cont.mean <- 130
drug.mean <- 120
cont.sd <- 15
drug.sd <- 15
alpha.error <- .05
beta.error <- .05
alternative <- "not equal" # also try "greater" or "less"
# find the correct function
# assign the correct parameters
# you can look around the documentation of pwrss library using ??pwrss
t.test.power <-
plot(t.test.power)
# Paired t-test ----------------------------------------------------------------
# parameters
before.mean <- 130
after.mean <- 120
before.sd <- 15
after.sd <- 15
alpha.error <- .05
beta.error <- .05
alternative <- "not equal" # also try "greater" or "less"
# find the correct function
# assign the correct parameters
# you can look around the documentation of pwrss library using ??pwrss
paired.t.power <-
plot(paired.t.power)
ex_01_params_solution.R → ex01/ex_01_params_solution.R
+ 0
− 0
View file @ 68f3e9d5
File moved
ex_02_sesoi.R → ex02/ex_02_sesoi.R
+ 20
− 20
View file @ 68f3e9d5
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwr)
# Find SESOI -------------------------------------------------------------------
previous.sample <- 25
alpha.level <- .01
beta.level <- .05
# find the SESOI
# ask for help using ??pwr
sesoi.eff <-
# Find power -------------------------------------------------------------------
# run a power analysis using the SESOI
# ask for help using ??pwr
# Packages ---------------------------------------------------------------------
list.of.packages <- c("pwr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(pwr)
# Find SESOI -------------------------------------------------------------------
previous.sample <- 25
alpha.level <- .01
beta.level <- .05
# find the SESOI
# ask for help using ??pwr
sesoi.eff <-
# Find power -------------------------------------------------------------------
# run a power analysis using the SESOI
# ask for help using ??pwr
ex_02_sesoi_solution.R → ex02/ex_02_sesoi_solution.R
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ex_03_simulation.R → ex03/ex_03_simulation.R
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# Packages ---------------------------------------------------------------------
list.of.packages <- c("ggplot2")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(ggplot2)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <-
cohen.d <-
sd <-
alpha.error <-
beta.error <-
sample.sizes <-
# Simulation ------------------------------------------------------------------
#create storage so you track every sample size's power
#for every sample size (try to iterate over the indices)
# create a storage vector for pvals
# create a storage vector for statistic
# for every simulation
# simulate random normally distributed samples for group 1 and add noise
# simulate random normally distributed samples for group 2, add your effect and noise
# do an independent t-test
# store statistic and pvals
# count how many results where significant
# print when your results was above your threshold
# think of when you sample sizes become larger than the power you want to achieve
# Packages ---------------------------------------------------------------------
list.of.packages <- c("ggplot2")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(ggplot2)
set.seed(2024)
# Simulation parameters --------------------------------------------------------
n.sim <-
cohen.d <-
sd <-
alpha.error <-
beta.error <-
sample.sizes <-
# Simulation ------------------------------------------------------------------
#create storage so you track every sample size's power
#for every sample size (try to iterate over the indices)
# create a storage vector for pvals
# create a storage vector for statistic
# for every simulation
# simulate random normally distributed samples for group 1 and add noise
# simulate random normally distributed samples for group 2, add your effect and noise
# do an independent t-test
# store statistic and pvals
# count how many results where significant
# print when your results was above your threshold
# think of when you sample sizes become larger than the power you want to achieve
ex_03_simulation_solution.R → ex03/ex_03_simulation_solution.R
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ex_04_seq_analysis.R → ex04/ex_04_seq_analysis.R
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ex_04_seq_analysis_solution.R → ex04/ex_04_seq_analysis_solution.R
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