ci_single_mean_theo <- function(y, conf_level, y_name,
show_var_types, show_summ_stats, show_res,
show_eda_plot, show_inf_plot){
# calculate sample size
n <- length(y)
# calculate x-bar
y_bar <- mean(y)
# define degrees of freedom
df <- n - 1
# find percentile associated with critical value
perc_crit_value <- conf_level + ((1 - conf_level) / 2)
# find critical value
t_star <- qt(perc_crit_value, df)
# calculate s
s <- sd(y)
# calculate SE
se <- s / sqrt(n)
# calculate ME
me <- t_star * se
# calculate CI
ci <- y_bar + c(-1, 1)* me
# print variable types
if(show_var_types == TRUE){
cat("Single numerical variable\n")
}
# print summary statistics
if(show_summ_stats == TRUE){
cat(paste0("n = ", n, ", y-bar = ", round(y_bar, 4), ", s = ", round(s, 4), "\n"))
}
# print results
if(show_res == TRUE){
conf_level_perc = conf_level * 100
cat(paste0(conf_level_perc, "% CI: (", round(ci[1], 4), " , ", round(ci[2], 4), ")\n"))
}
# eda_plot
d_eda <- data.frame(y = y)
eda_plot <- ggplot2::ggplot(data = d_eda, ggplot2::aes(x = y), environment = environment()) +
ggplot2::geom_histogram(fill = "#8FDEE1", binwidth = diff(range(y)) / 20) +
ggplot2::xlab(y_name) +
ggplot2::ylab("") +
ggplot2::ggtitle("Sample Distribution") +
ggplot2::geom_vline(xintercept = y_bar, col = "#1FBEC3", lwd = 1.5)
# print plots
if(show_eda_plot){ print(eda_plot) }
if(show_inf_plot){ warning("No inference plot available.", call. = FALSE) }
# return
return(list(df = df, SE = se, ME = me, CI = ci))
}
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