R/audpc.R

In myaseen208/agricolae: Statistical Procedures for Agricultural Research

#' @name    audpc
#' @aliases audpc
#' @title Calculating the absolute or relative value of the AUDPC
#' @description
#' Area Under Disease Progress Curve. The AUDPC measures the disease throughout
#' a period. The AUDPC is the area that is determined by the sum of trapezes
#' under the curve.
#' 
#' AUDPC. For the illustration one considers three evaluations (14, 21 and 28
#' days) and percentage of damage in the plant 40, 80 and 90 (interval between
#' dates of evaluation 7 days).  AUDPC = 1045.  The evaluations can be at
#' different interval.
#' 
#' @param evaluation Table of data of the evaluations: Data frame
#' @param dates Vector of dates corresponding to each evaluation
#' @param type relative, absolute
#' 
#' @return \strong{Vector} with relative or absolute audpc.
#' 
#' @author
#' \enumerate{
#'          \item Felipe de Mendiburu (\email{fmendiburu@@lamolina.edu.pe})
#'          \item Muhammad Yaseen (\email{myaseen208@@gmail.com})
#'          }
#' 
#' @references 
#' Campbell, C. L., L. V. Madden. (1990): Introduction to Plant
#' Disease Epidemiology. John Wiley & Sons, New York City.
#' 
#' @keywords manip
#' 
#' @export
#' 
#' @examples
#' 
#' library(agricolae)
#' dates<-c(14,21,28) # days
#' # example 1: evaluation - vector
#' evaluation<-c(40,80,90)
#' audpc(evaluation,dates)
#' # example 2: evaluation: dataframe nrow=1
#' evaluation<-data.frame(E1=40,E2=80,E3=90) # percentages
#' plot(dates,evaluation,type="h",ylim=c(0,100),col="red",axes=FALSE)
#' title(cex.main=0.8,main="Absolute or Relative AUDPC\nTotal area = 100*(28-14)=1400")
#' lines(dates,evaluation,col="red")
#' text(dates,evaluation+5,evaluation)
#' text(18,20,"A = (21-14)*(80+40)/2")
#' text(25,60,"B = (28-21)*(90+80)/2")
#' text(25,40,"audpc = A+B = 1015")
#' text(24.5,33,"relative = audpc/area = 0.725")
#' abline(h=0)
#' axis(1,dates)
#' axis(2,seq(0,100,5),las=2)
#' lines(rbind(c(14,40),c(14,100)),lty=8,col="green")
#' lines(rbind(c(14,100),c(28,100)),lty=8,col="green")
#' lines(rbind(c(28,90),c(28,100)),lty=8,col="green")
#' # It calculates audpc absolute
#' absolute<-audpc(evaluation,dates,type="absolute")
#' print(absolute)
#' rm(evaluation, dates, absolute)
#' # example 3: evaluation dataframe nrow>1
#' data(disease)
#' dates<-c(1,2,3) # week
#' evaluation<-disease[,c(4,5,6)]
#' # It calculates audpc relative
#' index <-audpc(evaluation, dates, type = "relative")
#' # Correlation between the yield and audpc
#' correlation(disease$yield, index, method="kendall")
#' # example 4: days infile
#' data(CIC)
#' comas <- CIC$comas
#' oxapampa <- CIC$oxapampa
#' dcomas <- names(comas)[9:16]
#' days<- as.numeric(substr(dcomas,2,3))
#' AUDPC<- audpc(comas[,9:16],days)
#' relative<-audpc(comas[,9:16],days,type = "relative")
#' h1<-graph.freq(AUDPC,border="red",density=4,col="blue")
#' table.freq(h1)
#' h2<-graph.freq(relative,border="red",density=4,col="blue",
#' frequency=2, ylab="relative frequency")
#' 

audpc <- 
  function(evaluation, dates, type = "absolute"){
    UseMethod("audpc")
  }

#' @export
#' @rdname audpc


audpc.default <-
function(evaluation, dates, type = "absolute") {
if(!(is.matrix(evaluation) | is.data.frame(evaluation))){
evaluation<-rbind(evaluation)
}
n<-length(dates)
k<-ncol(evaluation)
if (n!=k) {
cat("Error:\nThe number of dates of evaluation \nmust agree with the number of evaluations\n")
return()
}
audpc<-0
area.total<- 100*(dates[n]-dates[1])
for (i in 1:(n-1)) {
audpc<- audpc + (evaluation[,i]+evaluation[,i+1])*(dates[i+1]-dates[i])/2
}
if (type =="relative" ) audpc <-audpc/area.total
if (type =="absolute" | type =="relative" ) {
return(audpc)
}
else cat("Error: type is 'absolute' or 'relative'\n\n")
}