R/RSDT.R
In AljosjaK/singcar: Comparing Single Cases to Small Samples
#' Revised Standardised Difference Test
#'
#' A test on the discrepancy between two tasks in a single case, by comparison
#' to the discrepancy of means in the same two tasks in a control sample.
#' Standardises task scores as well as task discrepancy, so the tasks do not
#' need to be measured on the same scale. Calculates a standardised effect size
#' (Z-DCC) of task discrepancy as well as a point estimate of the proportion of
#' the control population that would be expected to show a more extreme
#' discrepancy. Developed by Crawford and Garthwaite (2005).
#'
#' @param case_a Case's score on task A.
#' @param case_b Case's score on task B.
#' @param controls_a Controls' scores on task A. Takes either a vector of
#' observations or a single value interpreted as mean. \emph{Note}: you can
#' supply a vector as input for task A while mean and SD for task B.
#' @param controls_b Controls' scores on task B. Takes either a vector of
#' observations or a single value interpreted as mean. \emph{Note}: you can
#' supply a vector as input for task B while mean and SD for task A.
#' @param sd_a If single value for task A is given as input you must
#' supply the standard deviation of the sample.
#' @param sd_b If single value for task B is given as input you must
#' supply the standard deviation of the sample.
#' @param sample_size If A or B is given as mean and SD you must supply the
#' sample size. If controls_a is given as vector and controls_b as mean and
#' SD, sample_size must equal the number of observations in controls_a.
#' @param r_ab If A or B is given as mean and SD you must supply the
#' correlation between the tasks.
#' @param alternative A character string specifying the alternative hypothesis,
#' must be one of \code{"two.sided"} (default), \code{"greater"} or
#' \code{"less"}. You can specify just the initial letter. Since the direction
#' of the expected effect depends on which task is set as A and which is set
#' as B, be very careful if changing this parameter.
#' @param na.rm Remove \code{NA}s from controls.
#'
#' @return A list with class \code{"htest"} containing the following components:
#' \tabular{llll}{ \code{statistic} \tab Returns the value of a approximate
#' t-statistic, however, because of the underlying equation, it cannot be
#' negative. See effect direction from Z-DCC. \cr\cr \code{parameter} \tab the
#' degrees of freedom for the t-statistic.\cr\cr \code{p.value} \tab the
#' p-value for the test.\cr\cr \code{estimate} \tab case scores expressed as
#' z-scores on task A and Y. Standardised effect size (Z-DCC) of task
#' difference between case and controls and point estimate of the proportion
#' of the control population estimated to show a more extreme task
#' discrepancy. \cr\cr \code{sample.size} \tab the size of the control
#' sample\cr\cr \code{null.value} \tab the value of the discrepancy under
#' the null hypothesis.\cr\cr \code{alternative} \tab a character string
#' describing the alternative hypothesis.\cr\cr \code{method} \tab a character
#' string indicating what type of test was performed.\cr\cr \code{data.name}
#' \tab a character string giving the name(s) of the data}
#' @export
#'
#' @examples
#' RSDT(-3.857, -1.875, controls_a = 0, controls_b = 0, sd_a = 1,
#' sd_b = 1, sample_size = 20, r_ab = 0.68)
#'
#' RSDT(case_a = size_weight_illusion[1, "V_SWI"], case_b = size_weight_illusion[1, "K_SWI"],
#' controls_a = size_weight_illusion[-1, "V_SWI"], controls_b = size_weight_illusion[-1, "K_SWI"])
#'
#' @references
#'
#' Crawford, J. R., & Garthwaite, P. H. (2005). Testing for
#' Suspected Impairments and Dissociations in Single-Case Studies in
#' Neuropsychology: Evaluation of Alternatives Using Monte Carlo Simulations and
#' Revised Tests for Dissociations. \emph{Neuropsychology, 19}(3), 318 - 331.
#' \doi{10.1037/0894-4105.19.3.318}
RSDT <- function (case_a, case_b, controls_a, controls_b,
sd_a = NULL, sd_b = NULL,
sample_size = NULL, r_ab = NULL,
alternative = c("two.sided", "greater", "less"),
na.rm = FALSE) {
###
# Set up of error and warning messages
###
case_a <- as.numeric(unlist(case_a))
controls_a <- as.numeric(unlist(controls_a))
case_b <- as.numeric(unlist(case_b))
controls_b <- as.numeric(unlist(controls_b))
if (length(case_a) > 1 | length(case_b) > 1) stop("Case scores should be single value")
if (length(controls_a) > 1 & length(controls_b) > 1) {
if (length(controls_a) != length(controls_b)) stop("Sample sizes must be equal")
}
if (length(controls_a) > 1 & length(controls_b) > 1 & is.null(sample_size) == FALSE) message("Value on sample_size will be ignored")
if (length(controls_a) > 1 & is.null(sd_a) == FALSE) message("Value on sd_a will be ignored")
if (length(controls_b) > 1 & is.null(sd_b) == FALSE) message("Value on sd_b will be ignored")
if (length(controls_a) == 1 & is.null(sd_a) == TRUE) stop("Please give sd and n on task A if controls_a is to be treated as mean")
if (length(controls_b) == 1 & is.null(sd_b) == TRUE) stop("Please give sd and n on task B if controls_b is to be treated as mean")
# Handling of NA use cases below
if(is.na(case_a) == TRUE | is.na(case_b) == TRUE) stop("One or both case scores is NA")
if (na.rm == TRUE) {
if (sum(is.na(controls_a)) > 0 & sum(is.na(controls_b)) == 0 ) {
controls_b <- controls_b[!is.na(controls_a)]
controls_a <- controls_a[!is.na(controls_a)]
warning("Removal of NAs on controls_a resulted in removal of non-NAs on controls_b")
}
if (sum(is.na(controls_b)) > 0 & sum(is.na(controls_a)) == 0 ) {
controls_a <- controls_a[!is.na(controls_b)]
controls_b <- controls_b[!is.na(controls_b)]
warning("Removal of NAs on controls_b resulted in removal of non-NAs on controls_a")
}
if (sum(is.na(controls_b)) > 0 & sum(is.na(controls_a)) > 0 ) {
if (identical(!is.na(controls_a), !is.na(controls_b)) == TRUE) {
controls_a <- controls_a[!is.na(controls_a)]
controls_b <- controls_b[!is.na(controls_b)]
} else {
con_a <- controls_a[!is.na(controls_a) & !is.na(controls_b)]
con_b <- controls_b[!is.na(controls_a) & !is.na(controls_b)]
controls_a <- con_a
controls_b <- con_b
warning("Removal of NAs on one control sample resulted in removal of non-NAs on the other")
}
}
}
if (sum(is.na(controls_a)) > 0 | sum(is.na(controls_b)) > 0) stop("Controls contains NA, set na.rm = TRUE to proceed")
# End of NA use cases
if (length(controls_a) > 1 & length(controls_b) > 1) {
if (length(controls_a) != length(controls_b)) stop("Sample sizes must be equal")
}
###
# Extract relevant statistics and set up further errors
###
alternative <- match.arg(alternative)
con_m_a <- mean(controls_a) # Mean of the control sample on task A
con_m_b <- mean(controls_b) # Mean of the control sample on task B
con_sd_a <- stats::sd(controls_a) # Standard deviation of the control sample on task A
if (length(controls_a) == 1 & is.null(sd_a) == FALSE) con_sd_a <- sd_a
con_sd_b <- stats::sd(controls_b) # Standard deviation of the control sample on task B
if (length(controls_b) == 1 & is.null(sd_b) == FALSE) con_sd_b <- sd_b
# Since controls x and y need to be of equal length n is the length of any of them
n <- length(controls_a)
if (length(controls_a) == 1 | length(controls_b) == 1) {
if (is.null(sample_size) == TRUE) stop("Please set sample size")
n <- sample_size
if (length(controls_a) > 1 & n != length(controls_a)) stop("Sample sizes must be equal")
if (length(controls_b) > 1 & n != length(controls_b)) stop("Sample sizes must be equal")
}
if (is.null(r_ab) == TRUE & length(controls_a) == 1) stop("Please set correlation between tasks")
if (is.null(r_ab) == TRUE & length(controls_b) == 1) stop("Please set correlation between tasks")
if (is.null(r_ab) == FALSE){
if (r_ab < -1 | r_ab > 1) stop("Correlation must be between -1 and 1")
}
r <- r_ab
if (length(controls_a) > 1 & length(controls_b) > 1) r <- stats::cor(controls_a, controls_b)
df <- n - 1 # Degrees of freedom
###
# Calculate standardised effect sizes
###
std_a <- (case_a - con_m_a)/con_sd_a
std_b <- (case_b - con_m_b)/con_sd_b
zdcc <- (std_a - std_b) / sqrt(2 - 2*r)
###
# Get approximate t-value, see Garthwaite & Crawford (2004)
###
a <- (1 + r)*(1 - r^2)
b <- (1 - r)*(
4*(n - 1)^2 + 4*(1 + r)*(n - 1) + (1 + r)*(5 + r)
)
c <- -2*(
((
(case_a - con_m_a)/con_sd_a -
(case_b - con_m_b)/con_sd_b
)^2) * (
(n*(n - 1)^2)/(n + 1)
)
)
tstat <- sqrt(
(-b + sqrt(b^2 - (4*a*c))) / (2*a)
)
names(tstat) <- "approx. abs. t"
###
# Get p-value
###
if (alternative == "two.sided") {
pval <- 2 * stats::pt(abs(tstat), df = df, lower.tail = FALSE)
if (zdcc < 0) {
p.name <- "Proportion below case (%)"
} else {
p.name <- "Proportion above case (%)"
}
} else if (alternative == "greater") {
# Since equation (7) from Crawford and Garthwaite (the exact method)
# cannot return a negative t-value we have to use zdcc to see
# in which direction the effect it pointing and the impose the correct sign.
if (zdcc > 0) {
pval <- stats::pt(tstat, df = df, lower.tail = FALSE)
} else {
pval <- stats::pt(-tstat, df = df, lower.tail = FALSE)
}
p.name <- "Proportion above case (%)"
}
else { # I.e. if alternative == "less"
if (zdcc < 0) {
pval <- stats::pt(-tstat, df = df, lower.tail = TRUE)
} else {
pval <- stats::pt(tstat, df = df, lower.tail = TRUE)
}
p.name <- "Proportion below case (%)"
}
# # Method from which the above is derived
# # Not included as functionality for now.
#
# if (alternative == "two.sided") {
# t.crit <- abs(stats::qt(alpha/2, df= df))
# } else if (alternative == "greater") {
# t.crit <- stats::qt(alpha, df= df, lower.tail = FALSE)
# } else {
# t.crit <- stats::qt(alpha, df= df)
# }
#
# denom <- sqrt(
# ((n + 1)/n) *
# (
# (2 - 2*r) + (
# 2*(1 - r^2)/(n - 1)
# ) + (
# ((5 + t.crit^2) * (1 - r^2))/(2*(n - 1)^2)
# ) + (
# (r*(1 + t.crit^2)*(1 - r^2))/((2*(n - 1)^2))
# )
# )
# )
#
# psi <- (std_a - std_b)/denom
#
# tstat <- psi
#
# names(tstat) <- "approx. t"
#
# if (alternative == "two.sided") {
# pval <- 2 * stats::pt(abs(psi), df = df, lower.tail = FALSE)
# } else if (alternative == "greater") {
# pval <- stats::pt(psi, df = df, lower.tail = FALSE)
# } else { # I.e. if alternative == "less"
# pval <- stats::pt(psi, df = df, lower.tail = TRUE)
# }
estimate <- c(std_a, std_b, zdcc, ifelse(alternative == "two.sided", (pval/2*100), pval*100))
###
# Set names for objects in output
###
names(estimate) <- c("Std. case score, task A (Z-CC)",
"Std. case score, task B (Z-CC)",
"Std. task discrepancy (Z-DCC)",
p.name)
names(df) <- "df"
null.value <- 0 # Null hypothesis: difference = 0
names(null.value) <- "difference between tasks"
dname <- paste0("Case A: ", format(round(case_a, 2), nsmall = 2), ", ",
"B: ", format(round(case_b, 2), nsmall = 2), ", ",
"Ctrl. A (m, sd): (", format(round(con_m_a, 2), nsmall = 2), ", ",format(round(con_sd_a, 2), nsmall = 2), "), ",
"B: (", format(round(con_m_b, 2), nsmall = 2), ", ",format(round(con_sd_b, 2), nsmall = 2), ")")
names(pval) <- NULL
# Build output to be able to set class as "htest" object for S3 methods.
# See documentation for "htest" class for more info
output <- list(statistic = tstat,
parameter = df,
p.value = pval,
estimate = estimate,
sample.size = n,
null.value = null.value,
alternative = alternative,
method = paste("Revised Standardised Difference Test"),
data.name = dname)
class(output) <- "htest"
output
}
AljosjaK/singcar documentation built on March 23, 2023, 1:40 p.m.
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#' Revised Standardised Difference Test
#'
#' A test on the discrepancy between two tasks in a single case, by comparison
#' to the discrepancy of means in the same two tasks in a control sample.
#' Standardises task scores as well as task discrepancy, so the tasks do not
#' need to be measured on the same scale. Calculates a standardised effect size
#' (Z-DCC) of task discrepancy as well as a point estimate of the proportion of
#' the control population that would be expected to show a more extreme
#' discrepancy. Developed by Crawford and Garthwaite (2005).
#'
#' @param case_a Case's score on task A.
#' @param case_b Case's score on task B.
#' @param controls_a Controls' scores on task A. Takes either a vector of
#' observations or a single value interpreted as mean. \emph{Note}: you can
#' supply a vector as input for task A while mean and SD for task B.
#' @param controls_b Controls' scores on task B. Takes either a vector of
#' observations or a single value interpreted as mean. \emph{Note}: you can
#' supply a vector as input for task B while mean and SD for task A.
#' @param sd_a If single value for task A is given as input you must
#' supply the standard deviation of the sample.
#' @param sd_b If single value for task B is given as input you must
#' supply the standard deviation of the sample.
#' @param sample_size If A or B is given as mean and SD you must supply the
#' sample size. If controls_a is given as vector and controls_b as mean and
#' SD, sample_size must equal the number of observations in controls_a.
#' @param r_ab If A or B is given as mean and SD you must supply the
#' correlation between the tasks.
#' @param alternative A character string specifying the alternative hypothesis,
#' must be one of \code{"two.sided"} (default), \code{"greater"} or
#' \code{"less"}. You can specify just the initial letter. Since the direction
#' of the expected effect depends on which task is set as A and which is set
#' as B, be very careful if changing this parameter.
#' @param na.rm Remove \code{NA}s from controls.
#'
#' @return A list with class \code{"htest"} containing the following components:
#' \tabular{llll}{ \code{statistic} \tab Returns the value of a approximate
#' t-statistic, however, because of the underlying equation, it cannot be
#' negative. See effect direction from Z-DCC. \cr\cr \code{parameter} \tab the
#' degrees of freedom for the t-statistic.\cr\cr \code{p.value} \tab the
#' p-value for the test.\cr\cr \code{estimate} \tab case scores expressed as
#' z-scores on task A and Y. Standardised effect size (Z-DCC) of task
#' difference between case and controls and point estimate of the proportion
#' of the control population estimated to show a more extreme task
#' discrepancy. \cr\cr \code{sample.size} \tab the size of the control
#' sample\cr\cr \code{null.value} \tab the value of the discrepancy under
#' the null hypothesis.\cr\cr \code{alternative} \tab a character string
#' describing the alternative hypothesis.\cr\cr \code{method} \tab a character
#' string indicating what type of test was performed.\cr\cr \code{data.name}
#' \tab a character string giving the name(s) of the data}
#' @export
#'
#' @examples
#' RSDT(-3.857, -1.875, controls_a = 0, controls_b = 0, sd_a = 1,
#' sd_b = 1, sample_size = 20, r_ab = 0.68)
#'
#' RSDT(case_a = size_weight_illusion[1, "V_SWI"], case_b = size_weight_illusion[1, "K_SWI"],
#' controls_a = size_weight_illusion[-1, "V_SWI"], controls_b = size_weight_illusion[-1, "K_SWI"])
#'
#' @references
#'
#' Crawford, J. R., & Garthwaite, P. H. (2005). Testing for
#' Suspected Impairments and Dissociations in Single-Case Studies in
#' Neuropsychology: Evaluation of Alternatives Using Monte Carlo Simulations and
#' Revised Tests for Dissociations. \emph{Neuropsychology, 19}(3), 318 - 331.
#' \doi{10.1037/0894-4105.19.3.318}
RSDT <- function (case_a, case_b, controls_a, controls_b,
sd_a = NULL, sd_b = NULL,
sample_size = NULL, r_ab = NULL,
alternative = c("two.sided", "greater", "less"),
na.rm = FALSE) {
###
# Set up of error and warning messages
###
case_a <- as.numeric(unlist(case_a))
controls_a <- as.numeric(unlist(controls_a))
case_b <- as.numeric(unlist(case_b))
controls_b <- as.numeric(unlist(controls_b))
if (length(case_a) > 1 | length(case_b) > 1) stop("Case scores should be single value")
if (length(controls_a) > 1 & length(controls_b) > 1) {
if (length(controls_a) != length(controls_b)) stop("Sample sizes must be equal")
}
if (length(controls_a) > 1 & length(controls_b) > 1 & is.null(sample_size) == FALSE) message("Value on sample_size will be ignored")
if (length(controls_a) > 1 & is.null(sd_a) == FALSE) message("Value on sd_a will be ignored")
if (length(controls_b) > 1 & is.null(sd_b) == FALSE) message("Value on sd_b will be ignored")
if (length(controls_a) == 1 & is.null(sd_a) == TRUE) stop("Please give sd and n on task A if controls_a is to be treated as mean")
if (length(controls_b) == 1 & is.null(sd_b) == TRUE) stop("Please give sd and n on task B if controls_b is to be treated as mean")
# Handling of NA use cases below
if(is.na(case_a) == TRUE | is.na(case_b) == TRUE) stop("One or both case scores is NA")
if (na.rm == TRUE) {
if (sum(is.na(controls_a)) > 0 & sum(is.na(controls_b)) == 0 ) {
controls_b <- controls_b[!is.na(controls_a)]
controls_a <- controls_a[!is.na(controls_a)]
warning("Removal of NAs on controls_a resulted in removal of non-NAs on controls_b")
}
if (sum(is.na(controls_b)) > 0 & sum(is.na(controls_a)) == 0 ) {
controls_a <- controls_a[!is.na(controls_b)]
controls_b <- controls_b[!is.na(controls_b)]
warning("Removal of NAs on controls_b resulted in removal of non-NAs on controls_a")
}
if (sum(is.na(controls_b)) > 0 & sum(is.na(controls_a)) > 0 ) {
if (identical(!is.na(controls_a), !is.na(controls_b)) == TRUE) {
controls_a <- controls_a[!is.na(controls_a)]
controls_b <- controls_b[!is.na(controls_b)]
} else {
con_a <- controls_a[!is.na(controls_a) & !is.na(controls_b)]
con_b <- controls_b[!is.na(controls_a) & !is.na(controls_b)]
controls_a <- con_a
controls_b <- con_b
warning("Removal of NAs on one control sample resulted in removal of non-NAs on the other")
}
}
}
if (sum(is.na(controls_a)) > 0 | sum(is.na(controls_b)) > 0) stop("Controls contains NA, set na.rm = TRUE to proceed")
# End of NA use cases
if (length(controls_a) > 1 & length(controls_b) > 1) {
if (length(controls_a) != length(controls_b)) stop("Sample sizes must be equal")
}
###
# Extract relevant statistics and set up further errors
###
alternative <- match.arg(alternative)
con_m_a <- mean(controls_a) # Mean of the control sample on task A
con_m_b <- mean(controls_b) # Mean of the control sample on task B
con_sd_a <- stats::sd(controls_a) # Standard deviation of the control sample on task A
if (length(controls_a) == 1 & is.null(sd_a) == FALSE) con_sd_a <- sd_a
con_sd_b <- stats::sd(controls_b) # Standard deviation of the control sample on task B
if (length(controls_b) == 1 & is.null(sd_b) == FALSE) con_sd_b <- sd_b
# Since controls x and y need to be of equal length n is the length of any of them
n <- length(controls_a)
if (length(controls_a) == 1 | length(controls_b) == 1) {
if (is.null(sample_size) == TRUE) stop("Please set sample size")
n <- sample_size
if (length(controls_a) > 1 & n != length(controls_a)) stop("Sample sizes must be equal")
if (length(controls_b) > 1 & n != length(controls_b)) stop("Sample sizes must be equal")
}
if (is.null(r_ab) == TRUE & length(controls_a) == 1) stop("Please set correlation between tasks")
if (is.null(r_ab) == TRUE & length(controls_b) == 1) stop("Please set correlation between tasks")
if (is.null(r_ab) == FALSE){
if (r_ab < -1 | r_ab > 1) stop("Correlation must be between -1 and 1")
}
r <- r_ab
if (length(controls_a) > 1 & length(controls_b) > 1) r <- stats::cor(controls_a, controls_b)
df <- n - 1 # Degrees of freedom
###
# Calculate standardised effect sizes
###
std_a <- (case_a - con_m_a)/con_sd_a
std_b <- (case_b - con_m_b)/con_sd_b
zdcc <- (std_a - std_b) / sqrt(2 - 2*r)
###
# Get approximate t-value, see Garthwaite & Crawford (2004)
###
a <- (1 + r)*(1 - r^2)
b <- (1 - r)*(
4*(n - 1)^2 + 4*(1 + r)*(n - 1) + (1 + r)*(5 + r)
)
c <- -2*(
((
(case_a - con_m_a)/con_sd_a -
(case_b - con_m_b)/con_sd_b
)^2) * (
(n*(n - 1)^2)/(n + 1)
)
)
tstat <- sqrt(
(-b + sqrt(b^2 - (4*a*c))) / (2*a)
)
names(tstat) <- "approx. abs. t"
###
# Get p-value
###
if (alternative == "two.sided") {
pval <- 2 * stats::pt(abs(tstat), df = df, lower.tail = FALSE)
if (zdcc < 0) {
p.name <- "Proportion below case (%)"
} else {
p.name <- "Proportion above case (%)"
}
} else if (alternative == "greater") {
# Since equation (7) from Crawford and Garthwaite (the exact method)
# cannot return a negative t-value we have to use zdcc to see
# in which direction the effect it pointing and the impose the correct sign.
if (zdcc > 0) {
pval <- stats::pt(tstat, df = df, lower.tail = FALSE)
} else {
pval <- stats::pt(-tstat, df = df, lower.tail = FALSE)
}
p.name <- "Proportion above case (%)"
}
else { # I.e. if alternative == "less"
if (zdcc < 0) {
pval <- stats::pt(-tstat, df = df, lower.tail = TRUE)
} else {
pval <- stats::pt(tstat, df = df, lower.tail = TRUE)
}
p.name <- "Proportion below case (%)"
}
# # Method from which the above is derived
# # Not included as functionality for now.
#
# if (alternative == "two.sided") {
# t.crit <- abs(stats::qt(alpha/2, df= df))
# } else if (alternative == "greater") {
# t.crit <- stats::qt(alpha, df= df, lower.tail = FALSE)
# } else {
# t.crit <- stats::qt(alpha, df= df)
# }
#
# denom <- sqrt(
# ((n + 1)/n) *
# (
# (2 - 2*r) + (
# 2*(1 - r^2)/(n - 1)
# ) + (
# ((5 + t.crit^2) * (1 - r^2))/(2*(n - 1)^2)
# ) + (
# (r*(1 + t.crit^2)*(1 - r^2))/((2*(n - 1)^2))
# )
# )
# )
#
# psi <- (std_a - std_b)/denom
#
# tstat <- psi
#
# names(tstat) <- "approx. t"
#
# if (alternative == "two.sided") {
# pval <- 2 * stats::pt(abs(psi), df = df, lower.tail = FALSE)
# } else if (alternative == "greater") {
# pval <- stats::pt(psi, df = df, lower.tail = FALSE)
# } else { # I.e. if alternative == "less"
# pval <- stats::pt(psi, df = df, lower.tail = TRUE)
# }
estimate <- c(std_a, std_b, zdcc, ifelse(alternative == "two.sided", (pval/2*100), pval*100))
###
# Set names for objects in output
###
names(estimate) <- c("Std. case score, task A (Z-CC)",
"Std. case score, task B (Z-CC)",
"Std. task discrepancy (Z-DCC)",
p.name)
names(df) <- "df"
null.value <- 0 # Null hypothesis: difference = 0
names(null.value) <- "difference between tasks"
dname <- paste0("Case A: ", format(round(case_a, 2), nsmall = 2), ", ",
"B: ", format(round(case_b, 2), nsmall = 2), ", ",
"Ctrl. A (m, sd): (", format(round(con_m_a, 2), nsmall = 2), ", ",format(round(con_sd_a, 2), nsmall = 2), "), ",
"B: (", format(round(con_m_b, 2), nsmall = 2), ", ",format(round(con_sd_b, 2), nsmall = 2), ")")
names(pval) <- NULL
# Build output to be able to set class as "htest" object for S3 methods.
# See documentation for "htest" class for more info
output <- list(statistic = tstat,
parameter = df,
p.value = pval,
estimate = estimate,
sample.size = n,
null.value = null.value,
alternative = alternative,
method = paste("Revised Standardised Difference Test"),
data.name = dname)
class(output) <- "htest"
output
}
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