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#' Welch's Test
#'
#' This function performs Welch's two-sample t-test and Welch's ANOVA including
#' Games-Howell post hoc test for multiple comparison and provides descriptive
#' statistics, effect size measures, and a plot showing error bars for
#' difference-adjusted confidence intervals with jittered data points.
#'
#' @param formula a formula of the form \code{y ~ group} where \code{y} is
#' a numeric variable giving the data values and \code{group}
#' a numeric variable, character variable or factor with two
#' or more than two values or factor levels giving the
#' corresponding groups.
#' @param data a matrix or data frame containing the variables in the
#' formula \code{formula}.
#' @param alternative a character string specifying the alternative hypothesis,
#' must be one of \code{"two.sided"} (default), \code{"greater"}
#' or \code{"less"}. Note that this argument is only used when
#' conducting Welch's two-sample t-test.
#' @param posthoc logical: if \code{TRUE}, Games-Howell post hoc test for
#' multiple comparison is conducted when performing Welch's
#' ANOVA.
#' @param conf.level a numeric value between 0 and 1 indicating the confidence
#' level of the interval.
#' @param hypo logical: if \code{TRUE} (default), null and alternative
#' hypothesis are shown on the console.
#' @param descript logical: if \code{TRUE} (default), descriptive statistics
#' are shown on the console.
#' @param effsize logical: if \code{TRUE}, effect size measure Cohen's d for
#' Welch's two-sample t-test (see \code{\link{cohens.d}}),
#' \eqn{\eta^2} and \eqn{\omega^2} for Welch's ANOVA and
#' Cohen's d for the post hoc tests are shown on the console.
#' @param weighted logical: if \code{TRUE}, the weighted pooled standard
#' deviation is used to compute Cohen's d.
#' @param ref a numeric value or character string indicating the reference
#' group. The standard deviation of the reference group is
#' used to standardized the mean difference to compute
#' Cohen's d.
#' @param correct logical: if \code{TRUE}, correction factor to remove
#' positive bias in small samples is used.
#' @param plot logical: if \code{TRUE}, a plot showing error bars for
#' confidence intervals is drawn.
#' @param point.size a numeric value indicating the \code{size} aesthetic for
#' the point representing the mean value.
#' @param adjust logical: if \code{TRUE} (default), difference-adjustment
#' for the confidence intervals is applied.
#' @param error.width a numeric value indicating the horizontal bar width of
#' the error bar.
#' @param xlab a character string specifying the labels for the x-axis.
#' @param ylab a character string specifying the labels for the y-axis.
#' @param ylim a numeric vector of length two specifying limits of the
#' limits of the y-axis.
#' @param breaks a numeric vector specifying the points at which tick-marks
#' are drawn at the y-axis.
#' @param jitter logical: if \code{TRUE} (default), jittered data points
#' are drawn.
#' @param jitter.size a numeric value indicating the \code{size} aesthetic
#' for the jittered data points.
#' @param jitter.width a numeric value indicating the amount of horizontal jitter.
#' @param jitter.height a numeric value indicating the amount of vertical jitter.
#' @param jitter.alpha a numeric value indicating the opacity of the jittered
#' data points.
#' @param title a character string specifying the text for the title for
#' the plot.
#' @param subtitle a character string specifying the text for the subtitle for
#' the plot.
#' @param digits an integer value indicating the number of decimal places
#' to be used for displaying descriptive statistics and
#' confidence interval.
#' @param p.digits an integer value indicating the number of decimal places
#' to be used for displaying the \emph{p}-value.
#' @param as.na a numeric vector indicating user-defined missing values,
#' i.e. these values are converted to \code{NA} before conducting
#' the analysis.
#' @param write a character string naming a text file with file extension
#' \code{".txt"} (e.g., \code{"Output.txt"}) for writing the
#' output into a text file.
#' @param append logical: if \code{TRUE} (default), output will be appended
#' to an existing text file with extension \code{.txt} specified
#' in \code{write}, if \code{FALSE} existing text file will be
#' overwritten.
#' @param check logical: if \code{TRUE} (default), argument specification
#' is checked.
#' @param output logical: if \code{TRUE} (default), output is shown on the
#' console.
#' @param ... further arguments to be passed to or from methods.
#'
#' @details
#' \describe{
#' \item{\strong{Effect Size Measure}}{By default, Cohen's d based on the non-weighted
#' standard deviation (i.e., \code{weighted = FALSE}) which does not assume homogeneity
#' of variance is computed (see Delacre et al., 2021) when requesting an effect size
#' measure (i.e., \code{effsize = TRUE}). Cohen's d based on the pooled standard
#' deviation assuming equality of variances between groups can be requested by
#' specifying \code{weighted = TRUE}.}
#' }
#'
#' @author
#' Takuya Yanagida \email{takuya.yanagida@@univie.ac.at}
#'
#' @seealso
#' \code{\link{test.t}}, \code{\link{test.z}}, \code{\link{test.levene}},
#' \code{\link{aov.b}}, \code{\link{cohens.d}}, \code{\link{ci.mean.diff}},
#' \code{\link{ci.mean}}
#'
#' @references
#' Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). \emph{Statistics in psychology
#' - Using R and SPSS}. John Wiley & Sons.
#'
#' Delacre, M., Lakens, D., Ley, C., Liu, L., & Leys, C. (2021). Why Hedges' g*s
#' based on the non-pooled standard deviation should be reported with Welch's
#' t-test. https://doi.org/10.31234/osf.io/tu6mp
#'
#' @return
#' Returns an object of class \code{misty.object}, which is a list with following
#' entries:
#' \tabular{ll}{
#' \code{call} \tab function call \cr
#' \code{type} \tab type of analysis \cr
#' \code{sample} \tab type of sample, i.e., two- or multiple sample \cr
#' \code{formula} \tab formula \cr
#' \code{data} \tab data frame with the outcome and grouping variable \cr
#' \code{plot} \tab ggplot2 object for plotting the results \cr
#' \code{args} \tab specification of function arguments \cr
#' \code{result} \tab list of result table \cr
#' }
#'
#' @export
#'
#' @examples
#' dat1 <- data.frame(group1 = c(1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2),
#' group2 = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3),
#' y = c(3, 1, 4, 2, 5, 3, 2, 3, 6, 6, 3, NA))
#'
#' #----------------------------------------------------------------------------
#' # Two-Sample Design
#'
#' # Example 1a: Two-sided two-sample Welch-test
#' test.welch(y ~ group1, data = dat1)
#'
#' # Example 1b: One-sided two-sample Welch-test
#' test.welch(y ~ group1, data = dat1, alternative = "greater")
#'
#' # Example 1c: Two-sided two-sample Welch-test
#' # print Cohen's d with weighted pooled SD
#' test.welch(y ~ group1, data = dat1, effsize = TRUE)
#'
#' # Example 1d: Two-sided two-sample Welch-test
#' # print Cohen's d with unweighted pooled SD
#' test.welch(y ~ group1, data = dat1, effsize = TRUE, weighted = FALSE)
#'
#' # Example 1e: Two-sided two-sample Welch-test
#' # print Cohen's d with weighted pooled SD and
#' # small sample correction factor
#' test.welch(y ~ group1, data = dat1, effsize = TRUE, correct = TRUE)
#'
#' # Example 1f: Two-sided two-sample Welch-test
#' # print Cohen's d with SD of the reference group 1
#' test.welch(y ~ group1, data = dat1, effsize = TRUE,
#' ref = 1)
#'
#' # Example 1g: Two-sided two-sample Welch-test
#' # print Cohen's d with weighted pooled SD and
#' # small sample correction factor
#' test.welch(y ~ group1, data = dat1, effsize = TRUE,
#' correct = TRUE)
#'
#' # Example 1h: Two-sided two-sample Welch-test
#' # do not print hypotheses and descriptive statistics,
#' test.welch(y ~ group1, data = dat1, descript = FALSE, hypo = FALSE)
#'
#' # Example 1i: Two-sided two-sample Welch-test
#' # print descriptive statistics with 3 digits and p-value with 5 digits
#' test.welch(y ~ group1, data = dat1, digits = 3, p.digits = 5)
#'
#' \dontrun{
#' # Example 1j: Two-sided two-sample Welch-test
#' # plot results
#' test.welch(y ~ group1, data = dat1, plot = TRUE)
#'
#' # Load ggplot2 package
#' library(ggplot2)
#'
#' # Save plot, ggsave() from the ggplot2 package
#' ggsave("Two-sample_Welch-test.png", dpi = 600, width = 4, height = 6)
#'
#' # Example 1k: Two-sided two-sample Welch-test
#' # extract plot
#' p <- test.welch(y ~ group1, data = dat1, output = FALSE)$plot
#' p
#'
#' # Extract data
#' plotdat <- test.welch(y ~ group1, data = dat1, output = FALSE)$data
#'
#' # Draw plot in line with the default setting of test.welch()
#' ggplot(plotdat, aes(factor(group), y)) +
#' geom_point(stat = "summary", fun = "mean", size = 4) +
#' stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", width = 0.20) +
#' scale_x_discrete(name = NULL) +
#' labs(subtitle = "Two-Sided 95% Confidence Interval") +
#' theme_bw() + theme(plot.subtitle = element_text(hjust = 0.5))
#' }
#' #----------------------------------------------------------------------------
#' # Multiple-Sample Design
#'
#' # Example 2a: Welch's ANOVA
#' test.welch(y ~ group2, data = dat1)
#'
#' # Example 2b: Welch's ANOVA
#' # print eta-squared and omega-squared
#' test.welch(y ~ group2, data = dat1, effsize = TRUE)
#'
#' # Example 2c: Welch's ANOVA
#' # do not print hypotheses and descriptive statistics,
#' test.welch(y ~ group2, data = dat1, descript = FALSE, hypo = FALSE)
#'
#' \dontrun{
#' # Example 2d: Welch's ANOVA
#' # plot results
#' test.welch(y ~ group2, data = dat1, plot = TRUE)
#'
#' # Load ggplot2 package
#' library(ggplot2)
#'
#' # Save plot, ggsave() from the ggplot2 package
#' ggsave("Multiple-sample_Welch-test.png", dpi = 600, width = 4.5, height = 6)
#'
#' # Example 2e: Welch's ANOVA
#' # extract plot
#' p <- test.welch(y ~ group2, data = dat1, output = FALSE)$plot
#' p
#'
#' # Extract data
#' plotdat <- test.welch(y ~ group2, data = dat1, output = FALSE)$data
#'
#' # Draw plot in line with the default setting of test.welch()
#' ggplot(plotdat, aes(group, y)) +
#' geom_point(stat = "summary", fun = "mean", size = 4) +
#' stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", width = 0.20) +
#' scale_x_discrete(name = NULL) +
#' labs(subtitle = "Two-Sided 95% Confidence Interval") +
#' theme_bw() + theme(plot.subtitle = element_text(hjust = 0.5))
#' }
test.welch <- function(formula, data, alternative = c("two.sided", "less", "greater"),
posthoc = FALSE, conf.level = 0.95, hypo = TRUE, descript = TRUE,
effsize = FALSE, weighted = FALSE, ref = NULL, correct = FALSE,
plot = FALSE, point.size = 4, adjust = TRUE, error.width = 0.1,
xlab = NULL, ylab = NULL, ylim = NULL, breaks = ggplot2::waiver(),
jitter = TRUE, jitter.size = 1.25, jitter.width = 0.05,
jitter.height = 0, jitter.alpha = 0.1,
title = "", subtitle = "Confidence Interval",
digits = 2, p.digits = 4, as.na = NULL, write = NULL, append = TRUE,
check = TRUE, output = TRUE, ...) {
#_____________________________________________________________________________
#
# Initial Check --------------------------------------------------------------
# Check if input 'formula' is missing
if (isTRUE(missing(formula))) { stop("Please specify a formula using the argument 'formula'", call. = FALSE) }
# Check if input 'data' is missing
if (isTRUE(missing(data))) { stop("Please specify a matrix or data frame for the argument 'x'.", call. = FALSE) }
# Check if input 'data' is NULL
if (isTRUE(is.null(data))) { stop("Input specified for the argument 'data' is NULL.", call. = FALSE) }
#_____________________________________________________________________________
#
# Variables ------------------------------------------------------------------
var.formula <- all.vars(as.formula(formula))
# Grouping variable
group.var <- attr(terms(formula[-2L]), "term.labels")
# Outcome variable
y.var <- var.formula[-grep(group.var, var.formula)]
#_____________________________________________________________________________
#
# Input Check ----------------------------------------------------------------
# Check input 'check'
if (isTRUE(!is.logical(check))) { stop("Please specify TRUE or FALSE for the argument 'check'.", call. = FALSE) }
# ggplot2 package
if (isTRUE(!nzchar(system.file(package = "ggplot2")))) { warning("Package \"ggplot2\" is needed for drawing a bar chart, please install the package.", call. = FALSE) }
if (isTRUE(check)) {
# Check if variables are in the data
var.data <- !var.formula %in% colnames(data)
if (isTRUE(any(var.data))) {
stop(paste0("Variables specified in the the formula were not found in 'data': ",
paste(var.formula[which(var.data)], collapse = ", ")), call. = FALSE)
}
# Check if input 'formula' has only one grouping variable
if (isTRUE(length(group.var) != 1L)) { stop("Please specify a formula with only one grouping variable.", call. = FALSE) }
# Check if input 'formula' has only one outcome variable
if (isTRUE(length(y.var) != 1L)) { stop("Please specify a formula with only one outcome variable.", call. = FALSE) }
# Check input 'descript'
if (isTRUE(!is.logical(descript))) { stop("Please specify TRUE or FALSE for the argument 'descript'.", call. = FALSE) }
# Check input 'alternative'
if (isTRUE(!all(alternative %in% c("two.sided", "less", "greater")))) { stop("Character string in the argument 'alternative' does not match with \"two.sided\", \"less\", or \"greater\".", call. = FALSE) }
# Check input 'conf.level'
if (isTRUE(conf.level >= 1L || conf.level <= 0L)) { stop("Please specifiy a numeric value between 0 and 1 for the argument 'conf.level'.", call. = FALSE) }
# Check input 'effsize'
if (isTRUE(!is.logical(effsize))) { stop("Please specify TRUE or FALSE for the argument 'effsize'.", call. = FALSE) }
# Check input 'weighted'
if (isTRUE(!is.logical(weighted))) { stop("Please specify TRUE or FALSE for the argument 'weighted'.", call. = FALSE) }
# Check input 'correct'
if (isTRUE(!is.logical(correct))) { stop("Please specify TRUE or FALSE for the argument 'correct'.", call. = FALSE) }
# Check input 'hypo'
if (isTRUE(!is.logical(hypo))) { stop("Please specify TRUE or FALSE for the argument 'hypo'.", call. = FALSE) }
# Check input 'descript'
if (isTRUE(!is.logical(descript))) { stop("Please specify TRUE or FALSE for the argument 'descript'.", call. = FALSE) }
# Check input 'plot'
if (isTRUE(!is.logical(plot))) { stop("Please specify TRUE or FALSE for the argument 'plot'.", call. = FALSE) }
# Check input 'jitter'
if (isTRUE(!is.logical(jitter))) { stop("Please specify TRUE or FALSE for the argument 'jitter'.", call. = FALSE) }
# Check input 'digits'
if (isTRUE(digits %% 1L != 0L || digits < 0L)) { stop("Please specify a positive integer number for the argument 'digits'.", call. = FALSE) }
# Check input 'p.digits'
if (isTRUE(p.digits %% 1L != 0L || p.digits < 0L)) { stop("Please specify a positive integer number for the argument 'p.digits'.", call. = FALSE) }
# Check input 'write'
if (isTRUE(!is.null(write) && substr(write, nchar(write) - 3L, nchar(write)) != ".txt")) { stop("Please specify a character string with file extenstion '.txt' for the argument 'write'.") }
# Check input 'append'
if (isTRUE(!is.logical(append))) { stop("Please specify TRUE or FALSE for the argument 'append'.", call. = FALSE) }
# Check input 'output'
if (isTRUE(!is.logical(output))) { stop("Please specify TRUE or FALSE for the argument 'output'.", call. = FALSE) }
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Convert user-missing values into NA ####
if (isTRUE(!is.null(as.na))) { data[, y.var] <- .as.na(data[, y.var], na = as.na) }
#_____________________________________________________________________________
#
# Data -----------------------------------------------------------------------
# Outcome
y <- unlist(data[, y.var])
# Grouping
group <- factor(unlist(data[, group.var]))
# Sample
sample <- ifelse(length(levels(group)) == 2L, "two", "multiple")
#_____________________________________________________________________________
#
# Arguments ------------------------------------------------------------------
# Global variables
m <- low <- upp <- NULL
# Alternative hypothesis
if (isTRUE(all(c("two.sided", "less", "greater") %in% alternative))) { alternative <- "two.sided" }
#_____________________________________________________________________________
#
# Main Function --------------------------------------------------------------
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Two-sample Welch test ####
if (isTRUE(sample == "two")) {
# Descriptive statistics
ci <- misty::df.rename(misty::ci.mean.diff(formula = formula, data = data, paired = FALSE, adjust = adjust, alternative = alternative, conf.level = conf.level, check = FALSE, output = FALSE)$result,
from = c("between", "low", "upp"), to = c("group", "m.low", "m.upp"))
# Cohen's d
d <- misty::cohens.d(formula = formula, data = data, paired = FALSE, mu = 0L,
weighted = weighted, cor = TRUE, ref = ref, correct = correct,
alternative = alternative, conf.level = conf.level,
group = NULL, split = NULL, sort.var = FALSE,
check = FALSE, output = FALSE)$result
# Welch's test for two groups
welch <- t.test(formula = formula, data = data,
alternative = switch(alternative,
two.sided = "two.sided",
greater = "less",
less = "greater"),
var.equal = FALSE)
#...................
### Result object ####
result <- data.frame(cbind(ci[, c("group", "n", "nNA", "m", "sd", "m.diff")],
se = c(NA, welch$stderr), ci[, c("m.low", "m.upp")],
t = c(NA, welch$statistic)*-1L, df = c(NA, welch$parameter), pval = c(NA, welch$p.value),
d = d$d, d.low = d$low, d.upp = d$upp),
row.names = NULL)
sample <- "two"
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Welch's test for more than two groups ####
} else if (isTRUE(sample == "multiple")) {
#...................
### Descriptive statistics ####
ci <- misty::ci.mean(y, group = group, adjust = adjust, output = FALSE)$result[, -c(2L, 5L)]
#...................
### ANOVA table ####
aov.table <- summary(aov(y ~ group))[[1L]]
ss.m <- aov.table[["Sum Sq"]][1L]
df.m <- aov.table[["Df"]][1L]
ms.r <- aov.table[["Mean Sq"]][2L]
ss.t <- sum(aov.table[["Sum Sq"]])
#...................
### Eta squared ####
eta.sq <- ss.m / ss.t
#...................
### Omega squared ####
omega.sq <- (ss.m - df.m*ms.r) / (ss.t + ms.r)
omega.sq <- ifelse(omega.sq < 0L, 0L, omega.sq)
#...................
### Welch's ANOVA ####
welch <- oneway.test(formula = formula, data = data, var.equal = FALSE)
#...................
### Post-Hoc test ####
# Generate all pairwise combinations
combs <- combn(levels(group), m = 2L)
# Number of groups
groups <- length(levels(group))
# Sample size
n <- setNames(ci[["n"]], ci[["group"]])
# Mean and variance
means <- setNames(ci[["m"]], ci[["group"]])
vars <- setNames(ci[["sd"]]^2L, ci[["group"]])
###
# Conduct post-hoc test
result.ph <- lapply(1L:ncol(combs), function(x) {
# Mean
temp.m1 <- means[names(means) == combs[1L, x]]
temp.m2 <- means[names(means) == combs[2L, x]]
# Variance
temp.var1 <- vars[names(vars) == combs[1L, x]]
temp.var2 <- vars[names(vars) == combs[2L, x]]
# Sample size
temp.n1 <- n[names(n) == combs[1L, x]]
temp.n2 <- n[names(n) == combs[2L, x]]
# Mean difference
m.diff <- temp.m2 - temp.m1
# t-values
t <- abs(temp.m1 - temp.m2) / sqrt((temp.var1/ temp.n1) + (temp.var2 / temp.n2))
# Degrees of Freedom
df <- (temp.var1 / temp.n1 + temp.var2 / temp.n2)^2L / ((temp.var1 / temp.n1)^2L / (temp.n1 - 1L) + (temp.var2 / temp.n2)^2L / (temp.n2 - 1L))
# p-values
pval <- stats::ptukey(t * sqrt(2L), groups, df, lower.tail = FALSE)
# Sigma standard error
se <- sqrt(0.5 * (temp.var1 / temp.n1 + temp.var2 / temp.n2))
# Lower Confidence Limit
m.low <- lapply(1L:ncol(combs), function(x) { m.diff - stats::qtukey(p = 0.95, nmeans = groups, df = df) * se })[[1L]]
# Upper Confidence Limit
m.upp <- lapply(1L:ncol(combs), function(x) { m.diff + stats::qtukey(p = 0.95, nmeans = groups, df = df) * se })[[1L]]
###
# Cohen's d
data.temp <- data.frame(group, y)[which(group %in% c(combs[1L, x], combs[2L, x])), ]
# Drop factor levels
data.temp[, "group"] <- droplevels(data.temp[, "group"], except = c(combs[1L, x], combs[2L, x]))
cohen <- misty::cohens.d(y ~ group, data = data.temp, weighted = weighted, correct = correct, conf.level = conf.level,
check = FALSE, output = FALSE)$result
###
# Collect results
result.ph <- list(combs[1L, x], combs[2L, x], m.diff, se, m.low, m.upp, t, df, pval, d = cohen[2L, "d"], cohen[2L, "low"], cohen[2L, "upp"])
})
# Result table
result.ph <- data.frame(matrix(unlist(lapply(result.ph, function(x) { unlist(x) })), nrow = ncol(combs), byrow = TRUE,
dimnames = list(NULL, c("group1", "group2", "m.diff", "se", "m.low", "m.upp", "t", "df", "pval", "d", "d.low", "d.upp"))))
# Convert to numeric
result.ph[, c(3L:ncol(result.ph))] <- as.numeric(as.matrix(result.ph[, c(3L:ncol(result.ph))]))
#...................
### Result object ####
result <- list(descript = ci,
test = data.frame(F = welch$statistic,
df1 = welch$parameter["num df"], df2 = welch$parameter["denom df"],
pval = welch$p.value,
eta.sq = eta.sq, omega.sq = omega.sq, row.names = NULL),
posthoc = result.ph)
sample <- "multiple"
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Plot ####
#...................
### Plot data ####
# Confidence interval
plot.ci <- misty::ci.mean(data[, y.var], group = data[, group.var], adjust = adjust, conf.level = conf.level, output = FALSE)$result
plotdat <- data.frame(group = group, y = y, row.names = NULL)
# Plot Subtitle
if (isTRUE(subtitle == "Confidence Interval")) { subtitle <- paste0("Two-Sided ", round(conf.level * 100L, digits = 2L), "% Confidence Interval") }
# Create ggplot
p <- ggplot2::ggplot(plotdat, ggplot2::aes(group, y))
# Add jittered points
if (isTRUE(jitter)) { p <- p + ggplot2::geom_jitter(alpha = jitter.alpha, width = jitter.width, height = jitter.height, size = jitter.size) }
p <- p + ggplot2::geom_point(data = plot.ci, ggplot2::aes(group, m), stat = "identity", size = point.size) +
ggplot2::geom_errorbar(data = plot.ci, ggplot2::aes(group, m, ymin = low, ymax = upp), width = error.width) +
ggplot2::scale_x_discrete(name = xlab) +
ggplot2::scale_y_continuous(name = ylab, limits = ylim, breaks = breaks) +
ggplot2::theme_bw() +
ggplot2::labs(title = title, subtitle = subtitle) +
ggplot2::theme(plot.subtitle = ggplot2::element_text(hjust = 0.5),
plot.title = ggplot2::element_text(hjust = 0.5))
#...................
### Print plot ####
if (isTRUE(plot)) { suppressWarnings(print(p)) }
#_____________________________________________________________________________
#
# Return Object --------------------------------------------------------------
object <- list(call = match.call(),
type = "test.welch",
sample = sample,
data = data.frame(y, group, stringsAsFactors = FALSE),
formula = formula,
plot = p,
args = list(alternative = alternative, posthoc = posthoc,
conf.level = conf.level, hypo = hypo, descript = descript,
effsize = effsize, weighted = weighted, ref = ref, correct = correct,
plot = plot, point.size = point.size, error.width = error.width,
xlab = xlab, ylab = ylab, ylim = ylim, breaks = breaks,
jitter = jitter, jitter.size = jitter.size, jitter.width = jitter.width,
jitter.height = jitter.height, jitter.alpha = jitter.alpha,
title = title, subtitle = subtitle, digits = digits, p.digits = p.digits,
as.na = as.na, check = check, output = output),
result = result)
class(object) <- "misty.object"
#_____________________________________________________________________________
#
# Write Results --------------------------------------------------------------
if (isTRUE(!is.null(write))) {
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Text file ####
# Send R output to textfile
sink(file = write, append = ifelse(isTRUE(file.exists(write)), append, FALSE), type = "output", split = FALSE)
if (isTRUE(append && file.exists(write))) { write("", file = write, append = TRUE) }
# Print object
print(object, check = FALSE)
# Close file connection
sink()
}
#_____________________________________________________________________________
#
# Output ---------------------------------------------------------------------
if (isTRUE(output)) { print(object, check = FALSE) }
return(invisible(object))
}
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