R/result_objects.R
In EventStudyTools/api-wrapper.r: Event Study Analysis
# // Copyright (C) 2017 Simon Müller
# // This file is part of EventStudy
# //
# // EventStudy is free software: you can redistribute it and/or modify it
# // under the terms of the GNU General Public License as published by
# // the Free Software Foundation, either version 2 of the License, or
# // (at your option) any later version.
# //
# // EventStudy is distributed in the hope that it will be useful, but
# // WITHOUT ANY WARRANTY; without even the implied warranty of
# // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# // GNU General Public License for more details.
# //
# // You should have received a copy of the GNU General Public License
# // along with EventStudy If not, see <http://www.gnu.org/licenses/>.
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
AARResults <- R6::R6Class(classname="AARResults",
public = list(
#' @field aar_tbl AAR results.
aar_tbl = NULL,
#' @field statistics_tbl AAR test statistic results.
statistics_tbl = NULL,
#' @description Class initialization
#'
#' @param aar_tbl AAR result table.
#' @param statistics_tbl Table with statistics.
initialize = function(aar_tbl, statistics_tbl) {
self$aar_tbl <- aar_tbl
self$statistics_tbl <- statistics_tbl
},
#' @description Print key characteristics.
print = function() {
cat("AAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$aar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$aar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$aar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$aar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
cat(paste0("Test Statistics: ", paste0(unique(self$statistics_tbl$`Test Statistic`), collapse = ", "), "\n"))
},
#' @description Plots AAR results for each analysis group.
#'
#' @param group Subset to your analysed groups, else all groups will be plotted.
#' @param ci_statistics Statistic used for confidence intervals
#' @param p The desired p-value
#' @param ci_type type of CI band for ggplot2, available are band or ribbon.
#' @param xlab x-axis label
#' @param ylab y-axis label
#' @param facet should each group get its own plot (default = T)
#' @param ncol number of facet columns
plot = function(group = NULL,
ci_statistics = NULL,
p = .95,
ci_type = "two-sided",
xlab = "Event Window",
ylab = "Averaged Abnormal Returns",
facet = T,
ncol = 4) {
library(RColorBrewer)
ci_type <- rlang::arg_match(ci_type, c("two-sided", "lower", "upper"))
if (is.null(ci_statistics)) {
ci_statistics = self$statistics_tbl['Test Statistic'][[1]][1]
}
ci_statistics <- rlang::arg_match(ci_statistics, unique(self$statistics_tbl['Test Statistic'] %>% .[[1]]))
testthat::expect_true(p < 1, "p must be smaller than 1.")
ci_stat_tbl = self$confidence_interval(ci_statistics, p, ci_type)
# CRAN check
level <- eventTime <- lower <- upper <- NULL
pal <- RColorBrewer::brewer.pal(3, "Blues")
aar_plt = self$aar_tbl %>%
dplyr::mutate(`Day Relative to Event` = as.numeric(`Day Relative to Event`)) %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`, y = AAR), color = pal[3], size=1) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
if (nrow(ci_stat_tbl)) {
aar_plt <- aar_plt +
geom_ribbon(data=ci_stat_tbl, aes(x = `Day Relative to Event`, ymin = lower, ymax = upper), fill = pal[3], alpha = .25)
}
# facet wrap
if (facet) {
aar_plt <- aar_plt +
facet_wrap( ~ Group, ncol = ncol, scales = "free")
}
aar_plt
},
#' @description Plot Cumulative Abnormal Return. No test statistic is available.
#'
#' @param xlab x axis lab
#' @param ylab y axis lab
#' @param facet Shall the plot faceted by Group
#' @param ncol Number of cols when faceting.
#'
#' @export
plot_cumulative = function(xlab = "Event Window",
ylab = "Cumulative Averaged Abnormal Returns",
facet = T,
ncol = 4) {
pal <- RColorBrewer::brewer.pal(3, "Blues")
caar_plt = self$aar_tbl %>%
dplyr::group_by(Group) %>%
dplyr::mutate(CAAR = cumsum(AAR), .groups = "drop") %>%
dplyr::mutate(`Day Relative to Event` = as.numeric(`Day Relative to Event`)) %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`, y = CAAR), color = pal[3], size=1) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
# facet wrap
if (facet) {
caar_plt <- caar_plt +
facet_wrap( ~ Group, ncol = ncol, scales = "free")
}
caar_plt
},
#' @description Calculates Confidence band for given test statistic.
#'
#' @param statistic Chosen test statistics for calculation.
#' @param p Chosen p value.
#' @param ci_type Type of confidence interval.
confidence_interval = function(statistic = "Patell Z",
p = 0.95,
ci_type = "two-sided") {
if (ci_type == 'two-sided') {
p <- 0.5 + p / 2
if (stringr::str_ends(statistic, "Z")) {
zStar <- qnorm(p)
} else if(stringr::str_ends(statistic, "T")) {
zStar <- qt(p, unique(aar_result$aar_tbl$N))
}
}
self$statistics_tbl %>%
dplyr::filter(`Test Statistic` == statistic) -> statistics_tbl
if (length(statistics_tbl)) {
aar <- as.numeric(self$aar_tbl[["AAR"]])
statistics_tbl[['lower']] <- aar - abs(aar) * zStar / abs(as.numeric(statistics_tbl$Statistics))
statistics_tbl[['upper']] <- aar + abs(aar) * zStar / abs(as.numeric(statistics_tbl$Statistics))
statistics_tbl[["is_significant"]] = "Not Significant"
statistics_tbl %>%
dplyr::mutate(
is_significant = ifelse(lower > 0, "Significant", is_significant),
is_significant = ifelse(upper < 0, "Significant", is_significant),
) -> statistics_tbl
}
statistics_tbl
},
#' @description Plots a heatmap with test statistics on y axis and Day Relative to Event on x axis. Colorization is done according to significance according to given p.
#'
#' @param p Chosen p value.
#' @param ci_type CI type.
#'
#' @export
plot_test_statistics = function(p = 0.95,
ci_type = "two-sided") {
self$statistics_tbl$`Test Statistic` %>%
unique() %>%
purrr::map(.f=function(x) {
self$confidence_interval(statistic = x, p = p, ci_type = ci_type)
}) %>%
purrr::reduce(rbind) %>%
dplyr::select(Group, `Test Statistic`, `Day Relative to Event`, `P Values`, is_significant) %>%
ggplot() +
geom_tile(aes(x=`Day Relative to Event`, y=`Test Statistic`, fill=is_significant), color="white", size=1) +
facet_wrap( ~ Group, ncol = 4, scales = "free") +
scale_fill_manual(name=NULL, values = c("steelblue", "darkred")) +
theme(legend.position = "top")
}
)
)
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
ARResults <- R6::R6Class(classname="ARResults",
public = list(
#' @field ar_tbl AR result table.
ar_tbl = NULL,
#' Class initialization
#'
#' @param ar_tbl AR result table.
initialize = function(ar_tbl) {
self$ar_tbl <- ar_tbl
},
#' @description Print key characteristics.
print = function() {
cat("AR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$ar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$ar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$ar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$ar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
},
#' @description Plot abnormal returns in the event window of single or multiple
#' firms.
#'
#' @param firm set this parameter if just a subset of firms should be plotted
#' @param xlab x-axis label of the plot
#' @param ylab y-axis label
#' @param addAAR add aar line
#'
#' @return a ggplot2 object
plot = function(firm = NULL,
xlab = "",
ylab = "Abnormal Returns",
addAAR = F) {
# CRAN check
Firm <- eventTime <- y <- NULL
ar_tbl <- self$ar_tbl
if (!is.null(firm)) {
ar_tbl %>%
dplyr::filter(Firm == firm) -> ar_tbl
}
pal <- RColorBrewer::brewer.pal(3, "Blues")
ar_plt = ar_tbl %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`,
y = AR,
group = Firm),
color = pal[3], alpha = 0.5) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
if (addAAR) {
data.table::setnames(ar_tbl, "AR", "y")
ar_tbl %>%
dplyr::group_by(`Day Relative to Event`) %>%
dplyr::summarise(y = mean(y, na.rm = T)) -> aar_tbl
data.table::setnames(ar_tbl, "y", "AR")
ar_plt <- ar_plt +
geom_line(data = aar_tbl, aes(x = `Day Relative to Event`, y = y), color = "black")
}
ar_plt
}
)
)
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
CAResults <- R6::R6Class(classname="CAResults",
public = list(
#' @field car_tbl Car result table
car_tbl = NULL,
#' Class initialization
#'
#' @param car_tbl CAR result table.
initialize = function(car_tbl) {
self$car_tbl <- car_tbl
},
#' @description Print key characteristics.
print = function() {
cat("CAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$aar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$aar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$aar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$aar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
}
)
)
#' @name CAAR Results.
#'
#' @title An R6 object that contains CAAR results.
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
CAAResults <- R6::R6Class(classname="CAAResults",
public = list(
#' @field caar_tbl CAAR results.
caar_tbl = NULL,
#' @field statistics_tbl CAAR test statistic results.
statistics_tbl = NULL,
#' Class initialization
#'
#' @param caar_tbl CAAR result table.
#' @param statistics_tbl Table with statistics.
initialize = function(caar_tbl, statistics_tbl) {
self$caar_tbl <- caar_tbl
self$statistics_tbl <- statistics_tbl
},
#' @description Print key characteristics.
print = function() {
cat("CAAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$caar_tbl$`Grouping Variable`)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$caar_tbl$`Grouping Variable`), collapse = ", "), "\n"))
}
)
)
EventStudyTools/api-wrapper.r documentation built on April 16, 2023, 1:04 p.m.
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# // Copyright (C) 2017 Simon Müller
# // This file is part of EventStudy
# //
# // EventStudy is free software: you can redistribute it and/or modify it
# // under the terms of the GNU General Public License as published by
# // the Free Software Foundation, either version 2 of the License, or
# // (at your option) any later version.
# //
# // EventStudy is distributed in the hope that it will be useful, but
# // WITHOUT ANY WARRANTY; without even the implied warranty of
# // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# // GNU General Public License for more details.
# //
# // You should have received a copy of the GNU General Public License
# // along with EventStudy If not, see <http://www.gnu.org/licenses/>.
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
AARResults <- R6::R6Class(classname="AARResults",
public = list(
#' @field aar_tbl AAR results.
aar_tbl = NULL,
#' @field statistics_tbl AAR test statistic results.
statistics_tbl = NULL,
#' @description Class initialization
#'
#' @param aar_tbl AAR result table.
#' @param statistics_tbl Table with statistics.
initialize = function(aar_tbl, statistics_tbl) {
self$aar_tbl <- aar_tbl
self$statistics_tbl <- statistics_tbl
},
#' @description Print key characteristics.
print = function() {
cat("AAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$aar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$aar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$aar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$aar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
cat(paste0("Test Statistics: ", paste0(unique(self$statistics_tbl$`Test Statistic`), collapse = ", "), "\n"))
},
#' @description Plots AAR results for each analysis group.
#'
#' @param group Subset to your analysed groups, else all groups will be plotted.
#' @param ci_statistics Statistic used for confidence intervals
#' @param p The desired p-value
#' @param ci_type type of CI band for ggplot2, available are band or ribbon.
#' @param xlab x-axis label
#' @param ylab y-axis label
#' @param facet should each group get its own plot (default = T)
#' @param ncol number of facet columns
plot = function(group = NULL,
ci_statistics = NULL,
p = .95,
ci_type = "two-sided",
xlab = "Event Window",
ylab = "Averaged Abnormal Returns",
facet = T,
ncol = 4) {
library(RColorBrewer)
ci_type <- rlang::arg_match(ci_type, c("two-sided", "lower", "upper"))
if (is.null(ci_statistics)) {
ci_statistics = self$statistics_tbl['Test Statistic'][[1]][1]
}
ci_statistics <- rlang::arg_match(ci_statistics, unique(self$statistics_tbl['Test Statistic'] %>% .[[1]]))
testthat::expect_true(p < 1, "p must be smaller than 1.")
ci_stat_tbl = self$confidence_interval(ci_statistics, p, ci_type)
# CRAN check
level <- eventTime <- lower <- upper <- NULL
pal <- RColorBrewer::brewer.pal(3, "Blues")
aar_plt = self$aar_tbl %>%
dplyr::mutate(`Day Relative to Event` = as.numeric(`Day Relative to Event`)) %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`, y = AAR), color = pal[3], size=1) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
if (nrow(ci_stat_tbl)) {
aar_plt <- aar_plt +
geom_ribbon(data=ci_stat_tbl, aes(x = `Day Relative to Event`, ymin = lower, ymax = upper), fill = pal[3], alpha = .25)
}
# facet wrap
if (facet) {
aar_plt <- aar_plt +
facet_wrap( ~ Group, ncol = ncol, scales = "free")
}
aar_plt
},
#' @description Plot Cumulative Abnormal Return. No test statistic is available.
#'
#' @param xlab x axis lab
#' @param ylab y axis lab
#' @param facet Shall the plot faceted by Group
#' @param ncol Number of cols when faceting.
#'
#' @export
plot_cumulative = function(xlab = "Event Window",
ylab = "Cumulative Averaged Abnormal Returns",
facet = T,
ncol = 4) {
pal <- RColorBrewer::brewer.pal(3, "Blues")
caar_plt = self$aar_tbl %>%
dplyr::group_by(Group) %>%
dplyr::mutate(CAAR = cumsum(AAR), .groups = "drop") %>%
dplyr::mutate(`Day Relative to Event` = as.numeric(`Day Relative to Event`)) %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`, y = CAAR), color = pal[3], size=1) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
# facet wrap
if (facet) {
caar_plt <- caar_plt +
facet_wrap( ~ Group, ncol = ncol, scales = "free")
}
caar_plt
},
#' @description Calculates Confidence band for given test statistic.
#'
#' @param statistic Chosen test statistics for calculation.
#' @param p Chosen p value.
#' @param ci_type Type of confidence interval.
confidence_interval = function(statistic = "Patell Z",
p = 0.95,
ci_type = "two-sided") {
if (ci_type == 'two-sided') {
p <- 0.5 + p / 2
if (stringr::str_ends(statistic, "Z")) {
zStar <- qnorm(p)
} else if(stringr::str_ends(statistic, "T")) {
zStar <- qt(p, unique(aar_result$aar_tbl$N))
}
}
self$statistics_tbl %>%
dplyr::filter(`Test Statistic` == statistic) -> statistics_tbl
if (length(statistics_tbl)) {
aar <- as.numeric(self$aar_tbl[["AAR"]])
statistics_tbl[['lower']] <- aar - abs(aar) * zStar / abs(as.numeric(statistics_tbl$Statistics))
statistics_tbl[['upper']] <- aar + abs(aar) * zStar / abs(as.numeric(statistics_tbl$Statistics))
statistics_tbl[["is_significant"]] = "Not Significant"
statistics_tbl %>%
dplyr::mutate(
is_significant = ifelse(lower > 0, "Significant", is_significant),
is_significant = ifelse(upper < 0, "Significant", is_significant),
) -> statistics_tbl
}
statistics_tbl
},
#' @description Plots a heatmap with test statistics on y axis and Day Relative to Event on x axis. Colorization is done according to significance according to given p.
#'
#' @param p Chosen p value.
#' @param ci_type CI type.
#'
#' @export
plot_test_statistics = function(p = 0.95,
ci_type = "two-sided") {
self$statistics_tbl$`Test Statistic` %>%
unique() %>%
purrr::map(.f=function(x) {
self$confidence_interval(statistic = x, p = p, ci_type = ci_type)
}) %>%
purrr::reduce(rbind) %>%
dplyr::select(Group, `Test Statistic`, `Day Relative to Event`, `P Values`, is_significant) %>%
ggplot() +
geom_tile(aes(x=`Day Relative to Event`, y=`Test Statistic`, fill=is_significant), color="white", size=1) +
facet_wrap( ~ Group, ncol = 4, scales = "free") +
scale_fill_manual(name=NULL, values = c("steelblue", "darkred")) +
theme(legend.position = "top")
}
)
)
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
ARResults <- R6::R6Class(classname="ARResults",
public = list(
#' @field ar_tbl AR result table.
ar_tbl = NULL,
#' Class initialization
#'
#' @param ar_tbl AR result table.
initialize = function(ar_tbl) {
self$ar_tbl <- ar_tbl
},
#' @description Print key characteristics.
print = function() {
cat("AR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$ar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$ar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$ar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$ar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
},
#' @description Plot abnormal returns in the event window of single or multiple
#' firms.
#'
#' @param firm set this parameter if just a subset of firms should be plotted
#' @param xlab x-axis label of the plot
#' @param ylab y-axis label
#' @param addAAR add aar line
#'
#' @return a ggplot2 object
plot = function(firm = NULL,
xlab = "",
ylab = "Abnormal Returns",
addAAR = F) {
# CRAN check
Firm <- eventTime <- y <- NULL
ar_tbl <- self$ar_tbl
if (!is.null(firm)) {
ar_tbl %>%
dplyr::filter(Firm == firm) -> ar_tbl
}
pal <- RColorBrewer::brewer.pal(3, "Blues")
ar_plt = ar_tbl %>%
ggplot() +
geom_hline(yintercept = 0, color = "black", alpha = .5) +
geom_vline(xintercept = 0, color = "black", linetype = 2, alpha = .5) +
geom_line(aes(x = `Day Relative to Event`,
y = AR,
group = Firm),
color = pal[3], alpha = 0.5) +
scale_y_continuous(labels = scales::percent) +
xlab(xlab) +
ylab(ylab)
if (addAAR) {
data.table::setnames(ar_tbl, "AR", "y")
ar_tbl %>%
dplyr::group_by(`Day Relative to Event`) %>%
dplyr::summarise(y = mean(y, na.rm = T)) -> aar_tbl
data.table::setnames(ar_tbl, "y", "AR")
ar_plt <- ar_plt +
geom_line(data = aar_tbl, aes(x = `Day Relative to Event`, y = y), color = "black")
}
ar_plt
}
)
)
#' @name aar_results
#'
#' @title An R6 object that contains AAR results.
#'
#' @section Methods:
#'
#' \describe{
#' \item{\code{plot}}{This method plots aar results.}
#' }
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
CAResults <- R6::R6Class(classname="CAResults",
public = list(
#' @field car_tbl Car result table
car_tbl = NULL,
#' Class initialization
#'
#' @param car_tbl CAR result table.
initialize = function(car_tbl) {
self$car_tbl <- car_tbl
},
#' @description Print key characteristics.
print = function() {
cat("CAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$aar_tbl$Group)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$aar_tbl$Group), collapse = ", "), "\n"))
cat(paste0("Length Event Window: ", length(unique(self$aar_tbl$`Day Relative to Event`)), "\n"))
cat(paste0("Event Window: ", paste0(unique(self$aar_tbl$`Day Relative to Event`), collapse = ", "), "\n"))
}
)
)
#' @name CAAR Results.
#'
#' @title An R6 object that contains CAAR results.
#'
#' @format \code{\link[R6]{R6Class}} object.
#' @export
CAAResults <- R6::R6Class(classname="CAAResults",
public = list(
#' @field caar_tbl CAAR results.
caar_tbl = NULL,
#' @field statistics_tbl CAAR test statistic results.
statistics_tbl = NULL,
#' Class initialization
#'
#' @param caar_tbl CAAR result table.
#' @param statistics_tbl Table with statistics.
initialize = function(caar_tbl, statistics_tbl) {
self$caar_tbl <- caar_tbl
self$statistics_tbl <- statistics_tbl
},
#' @description Print key characteristics.
print = function() {
cat("CAAR Results\n")
cat("-----------\n")
cat(paste0("N Groups: ", length(unique(self$caar_tbl$`Grouping Variable`)), "\n"))
cat(paste0("Groups: ", paste0(unique(self$caar_tbl$`Grouping Variable`), collapse = ", "), "\n"))
}
)
)
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