# Calculating the absolute or relative value of the AUDPC 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. ```r audpc(evaluation, dates, type = "absolute") ``` ## Arguments - `evaluation`: Table of data of the evaluations: Data frame - `dates`: Vector of dates corresponding to each evaluation - `type`: relative, absolute ## Details 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. ## Returns Vector with relative or absolute audpc. ## References Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York City. ## Author(s) Felipe de Mendiburu ## Examples ```r 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