ci_two_mean_sim <- function(y, x, conf_level, y_name, x_name,
boot_method, nsim, seed,
show_var_types, show_summ_stats, show_res,
show_eda_plot, show_inf_plot){
# set seed
if(!is.null(seed)){ set.seed(seed) }
# calculate n1 and n2
ns <- by(y, x, length)
n1 <- as.numeric(ns[1])
n2 <- as.numeric(ns[2])
n <- n1 + n2
# calculate y-bar1 and y-bar2
y_bars <- by(y, x, mean)
y_bar1 <- as.numeric(y_bars[1])
y_bar2 <- as.numeric(y_bars[2])
# calculate difference in y-bars
y_bar_diff <- y_bar1 - y_bar2
# create bootstrap distribution
y1 <- y[x == levels(x)[1]]
y2 <- y[x == levels(x)[2]]
sim_dist <- rep(NA, nsim)
for(i in 1:nsim){
boot_samp1 <- sample(y1, size = n1, replace = TRUE)
boot_samp2 <- sample(y2, size = n2, replace = TRUE)
sim_dist[i] <- mean(boot_samp1) - mean(boot_samp2)
}
# for percentile method
if(boot_method == "perc"){
# calculate quantile cutoffs based on confidence level
lower_quantile <- (1-conf_level) / 2
upper_quantile <- conf_level + lower_quantile
# calculate quantiles of the bootstrap distribution
ci_lower <- as.numeric(quantile(sim_dist, lower_quantile))
ci_upper <- as.numeric(quantile(sim_dist, upper_quantile))
# put CI together
ci <- c(ci_lower, ci_upper)
}
# for standard error method
if(boot_method == "se"){
# define degrees of freedom
df <- min(n1 - 1, n2 - 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 SE
se <- sd(sim_dist)
# calculate ME
me <- t_star * se
# calculate CI
ci <- y_bar_diff + c(-1, 1) * me
}
# print variable types
if(show_var_types == TRUE){
n_x_levels <- length(levels(x))
cat(paste0("Response variable: numerical, Explanatory variable: categorical (", n_x_levels," levels)\n"))
}
# print summary statistics
gr1 <- levels(x)[1]
gr2 <- levels(x)[2]
if(show_summ_stats == TRUE){
sds <- by(y, x, sd)
s1 <- as.numeric(sds[1])
s2 <- as.numeric(sds[2])
cat(paste0("n_", gr1, " = ", n1, ", y_bar_", gr1, " = ", round(y_bar1, 4), ", s_", gr1, " = ", round(s1, 4), "\n"))
cat(paste0("n_", gr2, " = ", n2, ", y_bar_", gr2, " = ", round(y_bar2, 4), ", s_", gr2, " = ", round(s2, 4), "\n"))
}
# print results
if(show_res == TRUE){
conf_level_perc = conf_level * 100
cat(paste0(conf_level_perc, "% CI (", gr1 ," - ", gr2,"): (", round(ci[1], 4), " , ", round(ci[2], 4), ")\n"))
}
# eda_plot
d_eda <- data.frame(y = y, x = x)
d_means <- data.frame(y_bars = as.numeric(y_bars), x = levels(x))
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(x_name) +
ggplot2::ggtitle("Sample Distribution") +
ggplot2::geom_vline(data = d_means, ggplot2::aes(xintercept = y_bars), col = "#1FBEC3", lwd = 1.5) +
ggplot2::facet_grid(x ~ .)
# inf_plot
d_inf <- data.frame(sim_dist = sim_dist)
inf_plot <- ggplot2::ggplot(data = d_inf, ggplot2::aes(x = sim_dist), environment = environment()) +
ggplot2::geom_histogram(fill = "#CCCCCC", binwidth = diff(range(sim_dist)) / 20) +
ggplot2::annotate("rect", xmin = ci[1], xmax = ci[2], ymin = 0, ymax = Inf,
alpha = 0.3, fill = "#FABAB8") +
ggplot2::xlab("bootstrap differences in means") +
ggplot2::ylab("") +
ggplot2::ggtitle("Bootstrap Distribution") +
ggplot2::geom_vline(xintercept = ci, color = "#F57670", lwd = 1.5)
# print plots
if(show_eda_plot & !show_inf_plot){
print(eda_plot)
}
if(!show_eda_plot & show_inf_plot){
print(inf_plot)
}
if(show_eda_plot & show_inf_plot){
gridExtra::grid.arrange(eda_plot, inf_plot, ncol = 2)
}
# return
if(boot_method == "perc"){
return(list(sim_dist = sim_dist, CI = ci))
} else {
return(list(sim_dist = sim_dist, SE = se, ME = me, CI = ci))
}
}
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