#' @title My Effect Size Calculation Two
#' @description A simple tool to calculate pairwise effect sizes
#' It takes the dataframe that has already been gathered (using tidyr)
#' and summarised into variables that include mu(mean), var(variance) and
#' freq(count). It then applies Cohen's D equation to calculate effect size
#' and output both the scaled effect and the descriptor.
#'
#' It will apply this to data that is given in the "key", "value" format.
#' @param df with a column titled key, my, var and freq
#'
#' @export
my_es_2 <- function(df){
keys <- unique(df$key)
out <- c()
for( i in 1:length(keys)){
new <- df %>%
filter(key == keys[i])
lx <- new$freq[[1]]
ly <- new$freq[[2]]
med <- abs(new$mu[[1]] - new$mu[[2]])
csd <- lx * new$var[[1]] + ly * new$var[[2]]
csd <- csd / (lx + ly)
csd <- sqrt(csd)
cd <- med / csd
es <- cd
es_sigma <- sqrt((ly + lx) / (lx * ly) + es^2/(2 * (lx + ly)))
es_ci <- es_sigma * 1.96
statz <- cbind(es = es, es_sigma = es_sigma, es_ci = es_ci)
out<- cbind(out, statz)
}
out <- out%>%
as.data.frame() %>%
mutate(effect_size = case_when(
es >2~"Huge",
es > 1.2~ "Very large",
es > 0.8~"Large",
es > 0.5~"Medium",
es > 0.2~"Small",
TRUE~"Very Small"
)) %>%
add_column(.,keys)
(out)
}
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