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#' Perform Welch's t-test
#'
#' Performs a Welch's t-test and calculates p-values between two groups.
#'
#' @param mean1 a numeric vector that contains the means of group1.
#' @param mean2 a numeric vector that contains the means of group2.
#' @param sd1 a numeric vector that contains the standard deviations of group1.
#' @param sd2 a numeric vector that contains the standard deviations of group2.
#' @param n1 a numeric vector that contains the number of replicates used for the calculation of
#' each mean and standard deviation of group1.
#' @param n2 a numeric vector that contains the number of replicates used for the calculation of
#' each mean and standard deviation of group2.
#' @param log_values a logical value that indicates if values are log transformed. This determines
#' how fold changes are calculated. Default is \code{log_values = TRUE}.
#'
#' @return A data frame that contains the calculated differences of means, standard error, t
#' statistic and p-values.
#' @importFrom stats pt
#' @export
#'
#' @examples
#' ttest_protti(
#' mean1 = 10,
#' mean2 = 15.5,
#' sd1 = 1,
#' sd2 = 0.5,
#' n1 = 3,
#' n2 = 3
#' )
ttest_protti <- function(mean1, mean2, sd1, sd2, n1, n2, log_values = TRUE) {
std_error <- sqrt((sd1^2 / n1) + (sd2^2 / n2))
# Welch-Satterwhite equation to estimate the degrees of freedom
df <- ((sd1^2 / n1) + (sd2^2 / n2))^2 / (sd1^4 / (n1^2 * (n1 - 1)) + sd2^4 / (n2^2 * (n2 - 1)))
# fold change calculation
if (log_values == TRUE) {
diff <- mean1 - mean2
} else {
diff <- mean1 / mean2
}
# t statistic calculation
t <- (diff) / std_error
result <- data.frame(cbind(diff, std_error, t, 2 * pt(-abs(t), df)))
colnames(result) <- c("diff", "std_error", "t_statistic", "pval")
return(result)
}
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