Nothing
R/quantify.breaks.R
In AQEval: Air Quality Evaluation
############################################
#' @title quantify break-point/segments
############################################
#' @name quantify.breaks
#' @aliases quantBreakPoints quantBreakSegments
#' @description Quantify either break-points or
#' break-segment methods for pollutant time-series
#' @param data Data source, typically a data.frame or similar,
#' containing data-series to model and a paired time-stamp
#' data-series, named date.
#' @param pollutant The name of the data-series to
#' break-point or break-segment model.
#' @param breaks (Optional) The break-points and
#' confidence intervals to use when building either
#' break-point or break-segment models. If not supplied
#' these are build using \code{\link{findBreakPoints}}
#' and supplied arguments.
#' @param ylab Y-label term, by default pollutant.
#' @param xlab X-label term, by default date.
#' @param pt.col Point fill and line colours for plot,
#' defaults lightgrey and darkgrey.
#' @param line.col Line colour for plot, default red.
#' @param break.col Break-point/segment colour for plot, default
#' blue.
#' @param event An optional list of plot terms for an event
#' marker, applied to a vertical line and text label. List
#' items include: \code{x} the event date (YYYY-MM-DD format)
#' require for both line and label; \code{y} by default 0.9 x
#' y-plot range; \code{label} the label text, required for
#' label; \code{line.size} the line width, by default 0.5;
#' \code{font.size} the text size, by default 5; and,
#' \code{hjust} the label left/right justification, 0 left,
#' 0.5 centre, 1 right (default). See also examples below.
#' @param show What to show before returning the break-point
#' quantification mode, by default plot and report.
#' @param ... other parameters
#' @param seg.method (\code{quantBreakSegments} only) the
#' break-segment fitting method to use.
#' @param seg.seed (\code{quantBreakSegments} only) the
#' seed setting to use when fitting break-segments, default
#' \code{12345}.
#' @details \code{quantBreakPoints} and
#' \code{quantBreakSegments} both use
#' \code{strucchange} methods to identify potential
#' break-points in time-series, and then quantify
#' these as conventional break-points or break-segments,
#' respectively:
#' \itemize{
#' \item \strong{Finding Break-points} Using the
#' \code{strucchange} methods of Zeileis and colleagues
#' and independent change detection model, the functions
#' apply a rolling-window approach, assuming the first
#' window (or data subset) is without change, building a
#' statistical model of that, advancing the window,
#' building a second model and comparing these, and so on,
#' to identify the most likely points of change in a
#' larger data-series. See also \code{\link{findBreakPoints}}
#' \item \strong{Quantifying Break-points} Using the
#' supplied break-points to build a break-point model.
#' \item \strong{Quantifying Break-segments} Using the
#' confidence regions for the supplied break-points as the
#' starting points to build a break-segment model.
#' }
#' @author Karl Ropkins
#' @references
#' Regarding \code{strucchange} methods see in-package
#' documentation, e.g. \code{\link[strucchange]{breakpoints}},
#' and:
#'
#' Achim Zeileis, Friedrich Leisch, Kurt Hornik and Christian Kleiber
#' (2002). strucchange: An R Package for Testing for Structural Change
#' in Linear Regression Models. Journal of Statistical Software, 7(2),
#' 1-38. URL \url{https://www.jstatsoft.org/v07/i02/}.
#'
#' Achim Zeileis, Christian Kleiber, Walter Kraemer and Kurt Hornik
#' (2003). Testing and Dating of Structural Changes in Practice.
#' Computational Statistics & Data Analysis, 44, 109-123.
#' DOI \doi{10.1016/S0167-9473(03)00030-6}.
#'
#' Regarding \code{segmented} methods see in-package
#' documentation, e.g.
#' \code{\link[segmented]{segmented}}, and:
#'
#' Vito M. R. Muggeo (2003). Estimating regression models
#' with unknown break-points. Statistics in Medicine, 22,
#' 3055-3071. DOI 10.1002/sim.1545.
#'
#' Vito M. R. Muggeo (2008). segmented: an R Package to
#' Fit Regression Models with Broken-Line Relationships.
#' R News, 8/1, 20-25.
#' URL \url{https://cran.r-project.org/doc/Rnews/}.
#'
#' Vito M. R. Muggeo (2016). Testing with a nuisance
#' parameter present only under the alternative: a
#' score-based approach with application to segmented
#' modelling. J of Statistical Computation and Simulation,
#' 86, 3059-3067.
#' DOI 10.1080/00949655.2016.1149855.
#'
#' Vito M. R. Muggeo (2017). Interval estimation for the
#' breakpoint in segmented regression: a smoothed
#' score-based approach. Australian & New Zealand Journal
#' of Statistics, 59, 311-322.
#' DOI 10.1111/anzs.12200.
#'
#' Regarding break-points/segment methods, see:
#'
#' Ropkins, K., Walker, A., Philips, I., Rushton, C., Clark, T. and
#' Tate, J., Change Detection of Air Quality Time-Series Using the
#' R Package AEQval. Available at SSRN 4267722.
#' https://ssrn.com/abstract=4267722 or http://dx.doi.org/10.2139/ssrn.4267722
#' Also at: https://karlropkins.github.io/AQEval/articles/AQEval_Intro_Preprint.pdf
#' @seealso
#' \code{\link[openair]{timeAverage}} in \code{openair},
#' \code{\link[strucchange]{breakpoints}} in \code{strucchange}, and
#' \code{\link[segmented]{segmented}} in \code{segmented}.
#'
#' @returns Both functions use the \code{show} argument
#' to control which elements of the functions outputs
#' are shown but also invisible return a \code{list}
#' of all outputs which can caught using, e.g.:
#'
#' \code{brk.mod <- quantBreakPoints(data, pollutant)}
#' @note \code{AQEval} function \code{quantBreakSegments}
#' is currently running \code{segmented v.1.3-4} while we
#' evaluate latest version, \code{v.1.4-0}.
#' @examples
#' #using openair timeAverage to covert 1-hour data to 1-day averages
#'
#' temp <- openair::timeAverage(aq.data, "1 day")
#'
#' #break-points
#'
#' quantBreakPoints(temp, "no2", h=0.3)
#'
#' #break-segments
#'
#' quantBreakSegments(temp, "no2", h=0.3)
#'
#' #addition examples (not run)
#' \dontrun{
#' #in-call plot modification
#' #removing x axis label
#' #recolouring break line and
#' #adding an event marker
#' quantBreakPoints(temp, "no2", h=0.3,
#' xlab="", break.col = "red",
#' event=list(label="Event expected here",
#' x="2002-08-01", col="grey"))
#'}
#quantBreakPoints
################################
#need to tidy
# most local functions using data2 rather than data
#recent rebuild to isolate model prediction
#splatted function
#' @rdname quantify.breaks
#' @export
quantBreakPoints <-
function (data, pollutant, breaks, ylab = NULL,
xlab = NULL, pt.col = c("lightgrey", "darkgrey"),
line.col = "red", break.col ="blue",
event = NULL, show = c("plot", "report"), ...)
{
##########################
#need a checker for date and pollutant
#does breaks default want to be NA
#some code in next section could move into subfunctions
##########################
data <- aqe_RowNameFix(data)
name.pol <- pollutant
pollutant <- data[, name.pol]
if(missing(breaks)){
#if breaks missing build assuming
#what sent plus defaults
breaks <- aqe_buildBreaks(data, name.pol, ...)
}
x.args <- list(...)
#print("fit")
################
data2 <- data[,c("date", name.pol)]
mod <- aqe_fitBreakPointsModel(data2, name.pol, breaks)
################
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
ans <- predict(mod, se.fit = TRUE)
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#print("report")
################
report <- aqe_makeBreakPointsReport(data2, breaks)
#################
#################
if ("report" %in% show) {
aqe_summariseBreakPointsReport(report)
}
#print("plot")
######
####################
#not sure this is best way to handle
#auto.text=FALSE...
#####################
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
plt <- aqe_plotQuantBreakPoints(data2, name.pol, breaks,
xlab=xlab, ylab=ylab, pt.col=pt.col,
line.col=line.col, break.col=break.col,
event=event, auto.text=auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
return(invisible(list(data = data, breaks = breaks, data2 = data2,
plot = plt, report = report, model = mod)))
}
#splatted function
#' @rdname quantify.breaks
#' @export
quantBreakSegments <-
function (data, pollutant, breaks, ylab = NULL,
xlab = NULL, pt.col = c("lightgrey", "darkgrey"),
line.col = "red", break.col ="blue",
event = NULL, seg.method = 2, seg.seed = 12345,
show = c("plot", "report"), ...)
{
##########################
#need a checker for date and pollutant like quantBreakPoints
#does breaks default want to be NA
#need to rationalise seg.method = 1/2 once method finalised
#see CRAN feedback regard set.seed
##########################
#breaks setup
#buildBreaks in quantBreakPoints
data <- aqe_RowNameFix(data)
if(missing(breaks)){
breaks <- aqe_buildBreaks(data, pollutant,...)
}
x.args <- list(...)
#model
if(!seg.method %in% 1:2){
stop("Unknown seg.method requested", call.=FALSE)
}
#######################################
#should be able to simplify this or
#drop it once seg.models confirmed
#see optimisation notes
#######################################
if(seg.method==1){
data <- aqe_RowNameFix(data)
mod <- aqe_fitBreakSegmentsModel01(data2, pollutant, breaks)
data2 <- data[,c("date", pollutant)]
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
ans <- predict(mod, se.fit = TRUE)
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#segments
#this needs to be tidied once methods finalised
segments <- aqe_makeSegmentsFromBreaks01(breaks)
#report
#this needs to be tidied once methods finalised
report <- aqe_makeBreakSegmentsReport(data2, segments)
if ("report" %in% show) {
aqe_summariseBreakSegmentsReport(report)
}
#plot
#this needs to be tidied once methods finalised
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
plt <- aqe_plotQuantBreakSegments01(data2, pollutant,
segments,
pt.col=pt.col,
break.col = break.col, line.col = line.col,
ylab=ylab, xlab=xlab,
event=event,
auto.text = auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
}
if(seg.method==2){
data2 <- data[,c("date", pollutant)]
#print("fit")
#################
data2 <- aqe_RowNameFix(data2)
ls.mod <- aqe_fitBreakSegmentsModel02(data2, pollutant, breaks,
seg.seed = seg.seed)
#print("after.fit")
mod <- ls.mod$mod
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
##########################
#not sure if I need all this
##########################
ans <- if("segmented" %in% class(mod)){
segmented::predict.segmented(mod, se.fit = TRUE)
} else {
predict(mod, se.fit=TRUE)
}
##############################
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#segments
#this needs to be tidied once methods finalised
segments <- ls.mod$segments
#report
#this needs to be tidied once methods finalised
#print("report")
###################
report <- aqe_makeBreakSegmentsReport(data2, segments)
if ("report" %in% show) {
aqe_summariseBreakSegmentsReport(report)
}
#plot
#this needs to be tidied once methods finalised
##plt <- NULL
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
#print("plot")
#######################
plt <- aqe_plotQuantBreakSegments02(data2, pollutant,
segments,
pt.col=pt.col,
break.col = break.col, line.col = line.col,
ylab=ylab, xlab=xlab,
event=event,
auto.text = auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
}
#output
return(invisible(list(data = data, segments = segments, data2 = data2,
plot = plt, report = report, model = mod)))
}
#################################
#removed
#################################
## @export
##quantBreaks02 <- function(...) quantBreakPoints(...)
## from days of quantBreaks01 and 02
## nobody should be using either these days
Try the AQEval package in your browser
Any scripts or data that you put into this service are public.
AQEval documentation built on April 3, 2025, 7:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
############################################
#' @title quantify break-point/segments
############################################
#' @name quantify.breaks
#' @aliases quantBreakPoints quantBreakSegments
#' @description Quantify either break-points or
#' break-segment methods for pollutant time-series
#' @param data Data source, typically a data.frame or similar,
#' containing data-series to model and a paired time-stamp
#' data-series, named date.
#' @param pollutant The name of the data-series to
#' break-point or break-segment model.
#' @param breaks (Optional) The break-points and
#' confidence intervals to use when building either
#' break-point or break-segment models. If not supplied
#' these are build using \code{\link{findBreakPoints}}
#' and supplied arguments.
#' @param ylab Y-label term, by default pollutant.
#' @param xlab X-label term, by default date.
#' @param pt.col Point fill and line colours for plot,
#' defaults lightgrey and darkgrey.
#' @param line.col Line colour for plot, default red.
#' @param break.col Break-point/segment colour for plot, default
#' blue.
#' @param event An optional list of plot terms for an event
#' marker, applied to a vertical line and text label. List
#' items include: \code{x} the event date (YYYY-MM-DD format)
#' require for both line and label; \code{y} by default 0.9 x
#' y-plot range; \code{label} the label text, required for
#' label; \code{line.size} the line width, by default 0.5;
#' \code{font.size} the text size, by default 5; and,
#' \code{hjust} the label left/right justification, 0 left,
#' 0.5 centre, 1 right (default). See also examples below.
#' @param show What to show before returning the break-point
#' quantification mode, by default plot and report.
#' @param ... other parameters
#' @param seg.method (\code{quantBreakSegments} only) the
#' break-segment fitting method to use.
#' @param seg.seed (\code{quantBreakSegments} only) the
#' seed setting to use when fitting break-segments, default
#' \code{12345}.
#' @details \code{quantBreakPoints} and
#' \code{quantBreakSegments} both use
#' \code{strucchange} methods to identify potential
#' break-points in time-series, and then quantify
#' these as conventional break-points or break-segments,
#' respectively:
#' \itemize{
#' \item \strong{Finding Break-points} Using the
#' \code{strucchange} methods of Zeileis and colleagues
#' and independent change detection model, the functions
#' apply a rolling-window approach, assuming the first
#' window (or data subset) is without change, building a
#' statistical model of that, advancing the window,
#' building a second model and comparing these, and so on,
#' to identify the most likely points of change in a
#' larger data-series. See also \code{\link{findBreakPoints}}
#' \item \strong{Quantifying Break-points} Using the
#' supplied break-points to build a break-point model.
#' \item \strong{Quantifying Break-segments} Using the
#' confidence regions for the supplied break-points as the
#' starting points to build a break-segment model.
#' }
#' @author Karl Ropkins
#' @references
#' Regarding \code{strucchange} methods see in-package
#' documentation, e.g. \code{\link[strucchange]{breakpoints}},
#' and:
#'
#' Achim Zeileis, Friedrich Leisch, Kurt Hornik and Christian Kleiber
#' (2002). strucchange: An R Package for Testing for Structural Change
#' in Linear Regression Models. Journal of Statistical Software, 7(2),
#' 1-38. URL \url{https://www.jstatsoft.org/v07/i02/}.
#'
#' Achim Zeileis, Christian Kleiber, Walter Kraemer and Kurt Hornik
#' (2003). Testing and Dating of Structural Changes in Practice.
#' Computational Statistics & Data Analysis, 44, 109-123.
#' DOI \doi{10.1016/S0167-9473(03)00030-6}.
#'
#' Regarding \code{segmented} methods see in-package
#' documentation, e.g.
#' \code{\link[segmented]{segmented}}, and:
#'
#' Vito M. R. Muggeo (2003). Estimating regression models
#' with unknown break-points. Statistics in Medicine, 22,
#' 3055-3071. DOI 10.1002/sim.1545.
#'
#' Vito M. R. Muggeo (2008). segmented: an R Package to
#' Fit Regression Models with Broken-Line Relationships.
#' R News, 8/1, 20-25.
#' URL \url{https://cran.r-project.org/doc/Rnews/}.
#'
#' Vito M. R. Muggeo (2016). Testing with a nuisance
#' parameter present only under the alternative: a
#' score-based approach with application to segmented
#' modelling. J of Statistical Computation and Simulation,
#' 86, 3059-3067.
#' DOI 10.1080/00949655.2016.1149855.
#'
#' Vito M. R. Muggeo (2017). Interval estimation for the
#' breakpoint in segmented regression: a smoothed
#' score-based approach. Australian & New Zealand Journal
#' of Statistics, 59, 311-322.
#' DOI 10.1111/anzs.12200.
#'
#' Regarding break-points/segment methods, see:
#'
#' Ropkins, K., Walker, A., Philips, I., Rushton, C., Clark, T. and
#' Tate, J., Change Detection of Air Quality Time-Series Using the
#' R Package AEQval. Available at SSRN 4267722.
#' https://ssrn.com/abstract=4267722 or http://dx.doi.org/10.2139/ssrn.4267722
#' Also at: https://karlropkins.github.io/AQEval/articles/AQEval_Intro_Preprint.pdf
#' @seealso
#' \code{\link[openair]{timeAverage}} in \code{openair},
#' \code{\link[strucchange]{breakpoints}} in \code{strucchange}, and
#' \code{\link[segmented]{segmented}} in \code{segmented}.
#'
#' @returns Both functions use the \code{show} argument
#' to control which elements of the functions outputs
#' are shown but also invisible return a \code{list}
#' of all outputs which can caught using, e.g.:
#'
#' \code{brk.mod <- quantBreakPoints(data, pollutant)}
#' @note \code{AQEval} function \code{quantBreakSegments}
#' is currently running \code{segmented v.1.3-4} while we
#' evaluate latest version, \code{v.1.4-0}.
#' @examples
#' #using openair timeAverage to covert 1-hour data to 1-day averages
#'
#' temp <- openair::timeAverage(aq.data, "1 day")
#'
#' #break-points
#'
#' quantBreakPoints(temp, "no2", h=0.3)
#'
#' #break-segments
#'
#' quantBreakSegments(temp, "no2", h=0.3)
#'
#' #addition examples (not run)
#' \dontrun{
#' #in-call plot modification
#' #removing x axis label
#' #recolouring break line and
#' #adding an event marker
#' quantBreakPoints(temp, "no2", h=0.3,
#' xlab="", break.col = "red",
#' event=list(label="Event expected here",
#' x="2002-08-01", col="grey"))
#'}
#quantBreakPoints
################################
#need to tidy
# most local functions using data2 rather than data
#recent rebuild to isolate model prediction
#splatted function
#' @rdname quantify.breaks
#' @export
quantBreakPoints <-
function (data, pollutant, breaks, ylab = NULL,
xlab = NULL, pt.col = c("lightgrey", "darkgrey"),
line.col = "red", break.col ="blue",
event = NULL, show = c("plot", "report"), ...)
{
##########################
#need a checker for date and pollutant
#does breaks default want to be NA
#some code in next section could move into subfunctions
##########################
data <- aqe_RowNameFix(data)
name.pol <- pollutant
pollutant <- data[, name.pol]
if(missing(breaks)){
#if breaks missing build assuming
#what sent plus defaults
breaks <- aqe_buildBreaks(data, name.pol, ...)
}
x.args <- list(...)
#print("fit")
################
data2 <- data[,c("date", name.pol)]
mod <- aqe_fitBreakPointsModel(data2, name.pol, breaks)
################
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
ans <- predict(mod, se.fit = TRUE)
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#print("report")
################
report <- aqe_makeBreakPointsReport(data2, breaks)
#################
#################
if ("report" %in% show) {
aqe_summariseBreakPointsReport(report)
}
#print("plot")
######
####################
#not sure this is best way to handle
#auto.text=FALSE...
#####################
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
plt <- aqe_plotQuantBreakPoints(data2, name.pol, breaks,
xlab=xlab, ylab=ylab, pt.col=pt.col,
line.col=line.col, break.col=break.col,
event=event, auto.text=auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
return(invisible(list(data = data, breaks = breaks, data2 = data2,
plot = plt, report = report, model = mod)))
}
#splatted function
#' @rdname quantify.breaks
#' @export
quantBreakSegments <-
function (data, pollutant, breaks, ylab = NULL,
xlab = NULL, pt.col = c("lightgrey", "darkgrey"),
line.col = "red", break.col ="blue",
event = NULL, seg.method = 2, seg.seed = 12345,
show = c("plot", "report"), ...)
{
##########################
#need a checker for date and pollutant like quantBreakPoints
#does breaks default want to be NA
#need to rationalise seg.method = 1/2 once method finalised
#see CRAN feedback regard set.seed
##########################
#breaks setup
#buildBreaks in quantBreakPoints
data <- aqe_RowNameFix(data)
if(missing(breaks)){
breaks <- aqe_buildBreaks(data, pollutant,...)
}
x.args <- list(...)
#model
if(!seg.method %in% 1:2){
stop("Unknown seg.method requested", call.=FALSE)
}
#######################################
#should be able to simplify this or
#drop it once seg.models confirmed
#see optimisation notes
#######################################
if(seg.method==1){
data <- aqe_RowNameFix(data)
mod <- aqe_fitBreakSegmentsModel01(data2, pollutant, breaks)
data2 <- data[,c("date", pollutant)]
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
ans <- predict(mod, se.fit = TRUE)
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#segments
#this needs to be tidied once methods finalised
segments <- aqe_makeSegmentsFromBreaks01(breaks)
#report
#this needs to be tidied once methods finalised
report <- aqe_makeBreakSegmentsReport(data2, segments)
if ("report" %in% show) {
aqe_summariseBreakSegmentsReport(report)
}
#plot
#this needs to be tidied once methods finalised
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
plt <- aqe_plotQuantBreakSegments01(data2, pollutant,
segments,
pt.col=pt.col,
break.col = break.col, line.col = line.col,
ylab=ylab, xlab=xlab,
event=event,
auto.text = auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
}
if(seg.method==2){
data2 <- data[,c("date", pollutant)]
#print("fit")
#################
data2 <- aqe_RowNameFix(data2)
ls.mod <- aqe_fitBreakSegmentsModel02(data2, pollutant, breaks,
seg.seed = seg.seed)
#print("after.fit")
mod <- ls.mod$mod
data2$pred <- rep(NA, nrow(data2))
data2$err <- data2$pred
##########################
#not sure if I need all this
##########################
ans <- if("segmented" %in% class(mod)){
segmented::predict.segmented(mod, se.fit = TRUE)
} else {
predict(mod, se.fit=TRUE)
}
##############################
data2$pred[as.numeric(names(ans$fit))] <- ans$fit
data2$err[as.numeric(names(ans$fit))] <- ans$se.fit
#segments
#this needs to be tidied once methods finalised
segments <- ls.mod$segments
#report
#this needs to be tidied once methods finalised
#print("report")
###################
report <- aqe_makeBreakSegmentsReport(data2, segments)
if ("report" %in% show) {
aqe_summariseBreakSegmentsReport(report)
}
#plot
#this needs to be tidied once methods finalised
##plt <- NULL
auto.text <- if("auto.text" %in% names(x.args)){
x.args$auto.text
} else { TRUE }
#print("plot")
#######################
plt <- aqe_plotQuantBreakSegments02(data2, pollutant,
segments,
pt.col=pt.col,
break.col = break.col, line.col = line.col,
ylab=ylab, xlab=xlab,
event=event,
auto.text = auto.text, ...)
if ("plot" %in% show) {
plot(plt)
}
}
#output
return(invisible(list(data = data, segments = segments, data2 = data2,
plot = plt, report = report, model = mod)))
}
#################################
#removed
#################################
## @export
##quantBreaks02 <- function(...) quantBreakPoints(...)
## from days of quantBreaks01 and 02
## nobody should be using either these days
Try the AQEval package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.