audpc {agricolae} | R Documentation |
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(evaluation, dates, type = "absolute")
evaluation |
Table of data of the evaluations: Data frame |
dates |
Vector of dates corresponding to each evaluation |
type |
relative, absolute |
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.
evaluation |
data frame |
dates |
a numeric vector |
type |
text |
Felipe de Mendiburu
Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York City.
library(agricolae) # example 1 dates<-c(14,21,28) # days 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 2 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$yield, index, method="kendall") # example 3 data(CIC) comas <- CIC$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")