audpc               package:agricolae               R Documentation

_C_a_l_c_u_l_a_t_i_n_g _t_h_e _a_b_s_o_l_u_t_e _o_r _r_e_l_a_t_i_v_e _v_a_l_u_e _o_f _t_h_e _A_U_D_P_C

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     audpc(evaluation, dates, type = "absolute")

_A_r_g_u_m_e_n_t_s:

evaluation: Table of data of the evaluations: Data frame 

   dates: Vector of dates corresponding to each evaluation 

    type: relative, absolute 

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

evaluation : data frame

  dates : a numeric vector

   type : text

_A_u_t_h_o_r(_s):

     Felipe de Mendiburu

_R_e_f_e_r_e_n_c_e_s:

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

_E_x_a_m_p_l_e_s:

     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")

