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#' Handling outliers in single-case data
#'
#' Identifies and drops outliers within a single-case data frame (scdf).
#'
#'
#' @inheritParams .inheritParams
#' @param method Specifies the method for outlier identification. Set `method =
#' "MAD"` for mean average deiviation, `method = "SD"` for standard
#' deviations, `method = "CI"` for confidence intervals, `method = "Cook"` for
#' Cook's Distance based on the Piecewise Linear Regression Model.
#'
#' @param criteria Specifies the criteria for outlier identification. Based on
#' the `method` setting.
#' @return \item{data}{A single-case data frame with substituted outliers.}
#' \item{dropped.n}{A list with the number of dropped data points for each
#' single-case.} \item{dropped.mt}{A list with the measurement-times of dropped
#' data points for each single-case (values are based on the `mt` variable
#' of each single-case data frame).} \item{sd.matrix}{A list with a matrix for
#' each case with values for the upper and lower boundaries based on the
#' standard deviation.} \item{ci.matrix}{A list with a matrix for each
#' single-case with values for the upper and lower boundaries based on the
#' confidence interval.} \item{cook}{A list of Cook's Distances for each
#' measurement of each single-case.} \item{criteria}{Criteria used for outlier
#' analysis.} \item{N}{Number of single-cases.} \item{case.names}{Case
#' identifier.}
#' @details For \code{method = "SD"}, \code{criteria = 2} would refer t0 two
#' standard deviations. For \code{method = "MAD"}, \code{criteria = 3.5} would
#' refer to 3.5 times the mean average deviation. For \code{method = "CI"},
#' \code{criteria = 0.99} would refer to a 99 percent confidence interval. For
#' \code{method = "cook"}, \code{criteria = "4/n"} would refer to a Cook's
#' Distance greater than 4/n.
#' @author Juergen Wilbert
#' @family data manipulation functions
#' @keywords manip
#' @examples
#'
#' ## Identify outliers using 1.5 standard deviations as criterion
#' susanne <- random_scdf(level = 1.0)
#' res_outlier <- outlier(susanne, method = "SD", criteria = 1.5)
#' plot(susanne, marks = res_outlier)
#'
#' ## Identify outliers in the original data from Grosche (2011)
#' ## using Cook's Distance greater than 4/n as criterion
#' res_outlier <- outlier(Grosche2011, method = "Cook", criteria = "4/n")
#' plot(Grosche2011, marks = res_outlier)
#'
#' @export
outlier <- function(data, dvar, pvar, mvar,
method = c("MAD", "Cook", "SD", "CI"),
criteria = 3.5) {
if (length(criteria) == 2) {
method <- criteria[1]
criteria <- criteria[2]
}
method <- match.arg(method)
check_args(
one_of(method, c("MAD", "Cook", "SD", "CI"))
)
# set defaults attributes
if (missing(dvar)) dvar <- dv(data)
if (missing(pvar)) pvar <- phase(data)
if (missing(mvar)) mvar <- mt(data)
dv(data) <- dvar
phase(data) <- pvar
mt(data) <- mvar
data_list <- .prepare_scdf(data)
out <- list()
N <- length(data_list)
case.names <- names(data_list)
dropped.mts <- list()
dropped.n <- list()
ci.matrix <- list()
sd.matrix <- list()
mad.matrix <- list()
cook <- list()
for(i in 1:N) {
data <- data_list[[i]]
phases <- rle(as.character(data[, pvar]))$value
values <- lapply(phases, function(x) data[data[, pvar] == x, dvar])
# CI ----------------------------------------------------------------------
if (identical(method, "CI")) {
cut_off <- as.numeric(criteria)
mat <- matrix(NA, length(values), ncol = 5)
colnames(mat) <- c("phase","m","se","lower", "upper")
rownames(mat) <- names(values)
filter <- c()
fac <- qnorm((1 - cut_off) / 2, lower.tail = FALSE)
for(p in 1:length(values)) {
x <- values[[p]]
mat[p,"m"] <- mean(x)
mat[p,"se"] <- sd(x)/sqrt(length(x))
mat[p,"lower"] <- mean(x) - fac * (sd(x)/sqrt(length(x)))
mat[p,"upper"] <- mean(x) + fac * (sd(x)/sqrt(length(x)))
filter <- c(filter, (x < mat[p, "lower"]) | (x > mat[p, "upper"]))
}
mat <- as.data.frame(mat)
mat$phase <- phases
ci.matrix[[i]] <- mat
}
# MAD ---------------------------------------------------------------------
if (identical(method, "MAD")) {
fac <- as.numeric(criteria)
mat <- matrix(NA, length(values), ncol = 5)
colnames(mat) <- c("phase", "md", "mad", "lower", "upper")
filter <- c()
for(p in 1:length(values)) {
x <- values[[p]]
mat[p,"md"] <- median(x)
mat[p,"mad"] <- mad(x,constant = 1)
mat[p,"lower"] <- median(x) - fac * mad(x)
mat[p,"upper"] <- median(x) + fac * mad(x)
filter <- c(filter, (x < mat[p,"lower"]) | (x > mat[p,"upper"]))
}
mat <- as.data.frame(mat)
mat$phase <- phases
mad.matrix[[i]] <- mat
}
# SD ----------------------------------------------------------------------
if (identical(method, "SD")) {
SD <- as.numeric(criteria)
mat <- matrix(NA, length(values), ncol = 5)
colnames(mat) <- c("phase", "m", "sd", "lower", "upper")
filter <- c()
for(p in 1:length(values)) {
x <- values[[p]]
mat[p,"m"] <- mean(x)
mat[p,"sd"] <- sd(x)
mat[p,"lower"] <- mean(x) - SD * sd(x)
mat[p,"upper"] <- mean(x) + SD * sd(x)
filter <- c(filter, (x < mat[p,"lower"]) | (x > mat[p, "upper"]))
}
mat <- as.data.frame(mat)
mat$phase <- phases
sd.matrix[[i]] <- mat
}
# Cook --------------------------------------------------------------------
if (identical(method, "Cook")) {
if (criteria == "4/n")
cut_off <- 4/nrow(data)
else
cut_off <- as.numeric(criteria)
reg <- plm(data_list[i], dvar = dvar, pvar = pvar, mvar = mvar)$full.model
cd <- cooks.distance(reg)
filter <- cd >= cut_off
cook[[i]] <- data.frame(Cook = round(cd, 2), MT = data_list[[i]][, mvar])
}
dropped.mts[[i]] <- data_list[[i]][filter, mvar]
dropped.n[[i]] <- sum(filter)
data_list[[i]] <- data_list[[i]][!filter, ]
}
out$data <- data_list
out$dropped.mt <- dropped.mts
out$dropped.n <- dropped.n
out$ci.matrix <- ci.matrix
out$sd.matrix <- sd.matrix
out$mad.matrix <- mad.matrix
out$cook <- cook
out$criteria <- criteria
out$N <- N
out$case.names <- case.names
class(out) <- c("sc_outlier")
attr(out, opt("phase")) <- pvar
attr(out, opt("mt")) <- mvar
attr(out, opt("dv")) <- dvar
out
}
#' @rdname deprecated-functions
#' @export
outlierSC <- function(...) {
.deprecated_warning("outlier", "outlierSC")
outlier(...)
}
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