R/data-trlremeasu.r

In biometrics: A Package for Biometrics and Modelling

#' Tree-level remeasurements for a sample plot in a _Pinus radiata_ plantation
#'
#' @description
#' Temporal tree-level data from a sample plot established in a
#' Monterey pine (_Pinus radiata_) forestry plantation in Chile.
#' The plot size is 1600 m\eqn{^{2}}{^2}, and the plantation was established
#' in 1990.
#' @usage
#' data(trlremeasu)
#' @format Tree list data for a sample plot remeasured through time, and
#' having the following columns
#' \describe{
#' \item{plot.id}{Plot code.}
#'   \item{tree}{Tree number.}
#' \item{x.coord}{Cartesian position in the X-axis, in m.}
#' \item{y.coord}{Cartesian position in the Y-axis, in m.}
#' \item{year}{Measurement year.}
#' \item{dead}{Dead identificator, `0` means alive, and `1` otherwise.}
#' \item{dbh}{diameter at breast-height, in cm.}
#'  }
#' @source
#' Data were retrieved from the paper cited below, where several details
#' might be worth reading.
#' @references
#' - Pommerening A, Trincado G, Salas-Eljatib C, Burkhart
#' H. 2023. Understanding and modelling the dynamics of data point
#' clouds of relative growth rate and plant size. Forest Ecology and
#' Management Volume 529:120652 \doi{10.1016/j.foreco.2022.120652}
#' 
#' @examples
#' data(trlremeasu)
#' head(trlremeasu)
#' df<-trlremeasu
#' df$fe<-10000/1600
#' df$garb.ha<- (pi/40000)*df$dbh^2*df$fe
#' gha.t<-tapply(df$garb.ha, df$year, sum)
#' nha.t<-tapply(df$fe, df$year, sum);
#' time<-as.numeric(rownames(gha.t))
#' plot(nha.t~time, type="b",las=1)
#' plot(gha.t~time, type="b",las=1)
'trlremeasu'