Nothing
## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(sprtt)
## ---- echo=TRUE---------------------------------------------------------------
d <- 0.2
## ---- echo=TRUE---------------------------------------------------------------
alpha <- 0.05
power <- 0.95
## ---- echo=TRUE---------------------------------------------------------------
paired <- TRUE
## ---- echo=TRUE---------------------------------------------------------------
alternative <- "greater"
## ---- echo=TRUE---------------------------------------------------------------
# first data from the Human Resources department ---
# current sample size
n_person <- 2
# get data
df <- df_stress[1:n_person,]
# print data
df
# sequential t-test
results <- seq_ttest(df$one_year_stress,
df$baseline_stress,
alpha = alpha,
power = power,
d = d,
paired = paired,
alternative = alternative,
verbose = FALSE)
# print results: console output
results
## ---- echo=TRUE---------------------------------------------------------------
results@decision
## ---- echo=TRUE---------------------------------------------------------------
# new data from the Human Resources department ---
# get one more person
n_person <- n_person + 1
df <- df_stress[1:n_person,]
# print new data
df
# sequential t-test
results <- seq_ttest(df$one_year_stress,
df$baseline_stress,
alpha = alpha,
power = power,
d = d,
paired = paired,
alternative = alternative,
verbose = FALSE)
# print results
results@decision
## ---- echo=TRUE---------------------------------------------------------------
# define the starting point
decision <- "continue sampling"
n_person <- 3
# simulation of the sequential procedure
while(decision == "continue sampling") {
# get the current data
df <- df_stress[1:n_person,]
# run the sequential test and save the results
results <- seq_ttest(df$one_year_stress,
df$baseline_stress,
alpha = alpha,
power = power,
d = d,
paired = paired,
alternative = alternative)
# save the current desicion
decision <- results@decision
# add a new person
n_person <- n_person + 1
# break if the maximum of the data is reached
if (n_person > nrow(df_stress)) {
break
}
}
# console output
results
## ---- echo=TRUE---------------------------------------------------------------
# Required results for the report
# likelihood ratio (LR)
LR <- round(results@likelihood_ratio, digits = 2)
LR
# sample size (N) = degrees of freedom +2 (two-samples) or +1 (one-sample & paired)
N <- results@df + 1
N
# baseline stress (M and SD)
mean_t1 <- round(mean(df$baseline_stress), digits = 2)
mean_t1
sd_t1 <- round(sd(df$baseline_stress), digits = 2)
sd_t1
# after one year stress (M and SD)
mean_t2 <- round(mean(df$one_year_stress), digits = 2)
mean_t2
sd_t2 <- round(sd(df$one_year_stress), digits = 2)
sd_t2
# NOT INCLUDED IN THE PACKAGE
# calculate effect size: Cohen´s d
d_results <- effsize::cohen.d(df$one_year_stress,
df$baseline_stress,
paired = TRUE)
d <- round(d_results$estimate, digits = 2)
d
# confidence intervall
d_lower <- round(d_results$conf.int[[1]], digits = 2)
d_lower
d_upper <- round(d_results$conf.int[[2]], digits = 2)
d_upper
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