#' Oxygen Consumption of Leukocytes
#'
#' A dataset containing measurements on the oxygen consumption of leukocytes in the presence and absence of inactivated staphylococci.
#'
#' @format A data frame with 144 rows and 5 variables:
#' \describe{
#' \item{O2}{oxygen consumption of leukocytes in \eqn{\mu}l}
#' \item{Staphylococci}{whether or not inactivated staphylococci were added, 1 denotes yes, 0 no}
#' \item{Time}{the measurements were taken after 6, 12 and 18 minutes}
#' \item{Group}{the treatment group, either P for Placebo or V for Verum}
#' \item{Subject}{the subject id}
#' }
#'
#' @usage data(o2cons)
#'
#' @source Friedrich, S., Brunner, E. & Pauly, M. (2017).
#' Permuting longitudinal data in spite of the dependencies.
#' Journal of Multivariate Analysis, 153, 255-265.
#'
#' @examples
#' if(requireNamespace("ggplot2")){
#' library(ggplot2)
#' ggplot(o2cons, aes(x=Group, y=O2)) + geom_point(alpha=0.5) + facet_grid(Staphylococci~Time) +
#' stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, colour = "red")
#' }
#'
"o2cons"
#' EEG Measurements in Patients with Alzheimer's Disease (long format)
#'
#' At the Department of Neurology, University Clinic of Salzburg, 160 patients were diagnosed
#' with either AD, MCI, or SCC, based on neuropsychological diagnostics. This data set contains z-scores for brain rate and Hjorth complexity,
#' each measured at frontal, temporal and central electrode positions and averaged across hemispheres. In addition to standardization, complexity
#' values were multiplied by -1 in order to make them more easily comparable to brain rate
#' values: For brain rate we know that the values decrease with age and pathology, while
#' Hjorth complexity values are known to increase with age and pathology.
#' The three between-subjects factors considered were sex (men vs. women), diagnosis (AD
#' vs. MCI vs. SCC), and age (\eqn{< 70} vs. \eqn{>= 70} years). Additionally, the within-subjects factors region (frontal, temporal, central) and
#' feature (brain rate, complexity) structure the response vector.
#'
#' @format A data frame with 960 rows and 7 variables:
#' \describe{
#' \item{resp}{EEG measurements}
#' \item{sex}{sex of the patient}
#' \item{age}{age of the patient, coded as 0 for less than 70 years and 1 for \eqn{>= 70} years}
#' \item{diagnosis}{neuropsychological diagnosis, AD for Alzheimer's Disease, MCI for mild cognitive impairment or SCC for subjective cognitive complaints without clinically significant deficits}
#' \item{region}{brain region of the EEG measurements, one of "temporal", "frontal" and "central"}
#' \item{feature}{feature of the EEG measurements, either "brainrate" or "complexity"}
#' \item{id}{Subject id}
#' }
#'
#' @usage data(EEG)
#'
#' @source Bathke, A., Friedrich, S., Konietschke, F., Pauly, M., Staffen, W., Strobl, N. and
#' Hoeller, Y. (2018). Testing Mean Differences among Groups: Multivariate and Repeated
#' Measures Analysis with Minimal Assumptions. Multivariate Behavioral Research.
#' Doi: 10.1080/00273171.2018.1446320.
#'
#' @examples
#' if(requireNamespace("ggplot2")){
#' library(ggplot2)
#' ggplot(EEG, aes(x=sex, y=resp)) + geom_point(alpha=0.5) + facet_grid(region+feature~diagnosis) +
#' stat_summary(fun.y = mean, fun.ymin = min, fun.ymax = max, colour = "red")
#' }
#'
"EEG"
#' EEG Measurements in Patients with Alzheimer's Disease (wide format)
#'
#' At the Department of Neurology, University Clinic of Salzburg, 160 patients were diagnosed
#' with either AD, MCI, or SCC, based on neuropsychological diagnostics. This data set contains z-scores for brain rate and Hjorth complexity,
#' each measured at frontal, temporal and central electrode positions and averaged across hemispheres. In addition to standardization, complexity
#' values were multiplied by -1 in order to make them more easily comparable to brain rate
#' values: For brain rate we know that the values decrease with age and pathology, while
#' Hjorth complexity values are known to increase with age and pathology.
#' The three between-subjects factors considered were sex (men vs. women), diagnosis (AD
#' vs. MCI vs. SCC), and age (\eqn{< 70} vs. \eqn{>= 70} years). Additionally, the within-subjects factors region (frontal, temporal, central) and
#' feature (brain rate, complexity) structure the response vector.
#'
#'@details Note that this data set contains exactly the same data as the data set 'EEG', only the format is different. The
#'transformation between the different formats can be achieved using, e.g., the \code{tidyr} package.
#'
#' @format A data frame with 160 rows and 9 variables:
#' \describe{
#' \item{brainrate_temporal}{EEG measurements for brainrate in temporal regions}
#' \item{brainrate_frontal}{EEG measurements for brainrate in frontal regions}
#' \item{brainrate_central}{EEG measurements for brainrate in central regions}
#' \item{complexity_temporal}{EEG measurements for complexity in temporal regions}
#' \item{complexity_frontal}{EEG measurements for complexity in frontal regions}
#' \item{complexity_central}{EEG measurements for complexity in central regions}
#' \item{sex}{sex of the patient}
#' \item{age}{age of the patient}
#' \item{diagnosis}{neuropsychological diagnosis, AD for Alzheimer's Disease, MCI for mild cognitive impairment or SCC for subjective cognitive complaints without clinically significant deficits}
#' \item{AgeGroup}{categorized age, coded as 0 for less than 70 years and 1 for \eqn{>= 70} years}
#' }
#'
#' @usage data(EEGwide)
#'
#' @source Bathke, A., Friedrich, S., Konietschke, F., Pauly, M., Staffen, W., Strobl, N. and
#' Hoeller, Y. (2018). Testing Mean Differences among Groups: Multivariate and Repeated
#' Measures Analysis with Minimal Assumptions. Multivariate Behavioral Research.
#' Doi: 10.1080/00273171.2018.1446320.
#'
#'
#' @examples
#' if(requireNamespace("ggplot2")){
#' library("ggplot2")
#' qplot(data = EEGwide, diagnosis)
#' }
#'
"EEGwide"
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