audpc {agricolae} | R Documentation

## 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.

### Usage

    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.

### Value

Vector with relative or absolute audpc.

### Author(s)

Felipe de Mendiburu

### References

Campbell, C. L., L. V. Madden. (1990): Introduction to Plant Disease Epidemiology. John Wiley & Sons, New York City.

### 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")

[Package agricolae version 1.2-9 Index]