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expert.R
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#This file is part of ElicitN.
#Copyright 2011 Rebecca Fisher and Rebecca O'Leary.
#ElicitN is free software: you can redistribute it and/or modify it under
#the terms of the GNU General Public License as published by the Free Software
#Foundation, either version 3 of the License, or any later version.
#This program is distributed in the hope that it will be useful, but WITHOUT ANY
#WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
#PARTICULAR PURPOSE. See the GNU General Public License
#(http://www.gnu.org/licenses/) for more details.
expert<-function(Lower.N, Upper.N, Best.N,
Lower.P, Upper.P, Best.P,
Lower.M, Upper.M, Best.M, Sureness,new.alpha,
expert.K.ALLnorm.results,best.type.status){
eval.text <- getURL("https://raw.githubusercontent.com/beckyfisher/ElicitN/master/expert.P.BETA.R", ssl.verifypeer = FALSE)
eval(parse(text = eval.text))
#source("expert.P.BETA.R")
eval.text <- getURL("https://raw.githubusercontent.com/beckyfisher/ElicitN/master/expert.K.noplot.ALLNorm.R", ssl.verifypeer = FALSE)
eval(parse(text = eval.text))
#source("expert.K.noplot.ALLNorm.R")
##############################################
#IF 1
#EXPERT PROVIDES % OF KNOWN SPECIES (INFLATION)
if (is.na(Best.P) =="FALSE") {
expert.P.BETA.results <- expert.P.BETA(Lower.P, Upper.P, Best.P, Sureness, new.alpha, best.type.status, ee.type=2)
#####
#PLOT
#PERCENT of KNOWN SPECIES
####
###
#find min & max for plot
plot.P.min <- min( expert.P.BETA.results$ssBETA.mode.results$lower,
expert.P.BETA.results$feedbackBETA.mode.results$new.lower)
plot.P.max <- max(expert.P.BETA.results$ssBETA.mode.results$upper,
expert.P.BETA.results$feedbackBETA.mode.results$new.upper)
####
#plot
par(mfrow=c(2,3), mar=c(1, 3.5, 4,.75)+.5, mgp=c(3, .75,0))
if (expert.P.BETA.results$ssBETA.mode.results$lower <0) {
expert.P.BETA.results$ssBETA.mode.results$lower =0
}
if (expert.P.BETA.results$feedbackBETA.mode.results$new.lower<0){
expert.P.BETA.results$feedbackBETA.mode.results$new.lower =0
}
plot.P.min <- min(
expert.P.BETA.results$ssBETA.mode.results$lower,
expert.P.BETA.results$feedbackBETA.mode.results$new.lower)
plot.P.max <- max(
expert.P.BETA.results$ssBETA.mode.results$upper,
expert.P.BETA.results$feedbackBETA.mode.results$new.upper)
plot(rep(c(1),2), rep(c(expert.P.BETA.results$ssBETA.mode.results$lower*100,
expert.P.BETA.results$ssBETA.mode.results$upper*100),1)
, xlim=c(-0.5,3), xlab="", ylab="%", type="l",
ylim=c((plot.P.min-plot.P.min*.1)*100, (plot.P.max+plot.P.max*.1)*100),
xaxt = "n", main="a", lwd=2)
lines(rep(2,2),c(expert.P.BETA.results$ssBETA.mode.results$lower*100,
expert.P.BETA.results$ssBETA.mode.results$upper*100), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$lower*100,2), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$upper*100,2), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$mode*100,2),col="red", lwd=2)
lines(c(1,2), rep(Best.P*100,2),col=1, lwd=2) #best guess
legend("topleft", lty=1, col=c(1, "red"), bty="n", legend=c("elicited parameters",
"estimated (fitted) "),cex=.8) #BEST GUESS IS MODE
legend("topright", paste("new
sureness =", new.alpha), cex=.8, bty="n")
#plot new lower & upper conf
lines(c(1,2), rep(expert.P.BETA.results$feedbackBETA.mode.results$new.lower*100,2), col="red" , lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$feedbackBETA.mode.results$new.upper*100,2), col="red", lwd=2)
###
# Component 1
#TO PLOT N (INFLATION (%) * K)
###
#pihat is returned from expert.K.LN function
pihat <- expert.K.ALLnorm.results$pihat
#which distribution was used in expert.K.ALLnorm.results
which.dist <- expert.K.ALLnorm.results$which.dist
MhatK <-expert.K.ALLnorm.results$MhatK
#get values of mu, sig, lower.K & upper.K
mu <- expert.K.ALLnorm.results$fit.best.mode.mu
sig <- expert.K.ALLnorm.results$fit.best.mode.sig
eval.text <- getURL("https://raw.githubusercontent.com/beckyfisher/ElicitN/master/feedback.AllNorm.R", ssl.verifypeer = FALSE)
eval(parse(text = eval.text))
#source("feedback.AllNorm.R")
feedback.AllNorm.results <- feedback.AllNorm(mu, sig,Sureness,new.alpha, MhatK, which.dist)
lower.K <- feedback.AllNorm.results$lower
upper.K <- feedback.AllNorm.results$upper
mode.K <- feedback.AllNorm.results$mode
new.lower <- feedback.AllNorm.results$new.lower
new.upper <- feedback.AllNorm.results$new.upper
Ksp <- feedback.AllNorm.results$Ksp
#calculate number of species for cryptic species
Cryp <- rbeta(10000,expert.P.BETA.results$fitBETA.best.mode.alpha, expert.P.BETA.results$fitBETA.best.mode.beta)
#calculate number of cryptic spp.
Cryp<- Ksp*(Cryp)
###
#PLOT
###
#MODE
if (expert.P.BETA.results$ssBETA.mode.results$lower <0) {
expert.P.BETA.results$ssBETA.mode.results$lower =0
}
if (expert.P.BETA.results$feedbackBETA.mode.results$new.lower<0){
expert.P.BETA.results$feedbackBETA.mode.results$new.lower =0
}
#find min & max for plot
plot.PN.min <- min(
expert.P.BETA.results$ssBETA.mode.results$lower * lower.K,
expert.P.BETA.results$feedbackBETA.mode.results$new.lower * lower.K)
plot.PN.max <- max(
expert.P.BETA.results$ssBETA.mode.results$upper* upper.K,
expert.P.BETA.results$feedbackBETA.mode.results$new.upper* new.upper)
plot(rep(c(1),2), rep(c(expert.P.BETA.results$ssBETA.mode.results$lower * lower.K,
expert.P.BETA.results$ssBETA.mode.results$upper * upper.K),1)
, xlim=c(-0.5,3), xlab="", ylab="N", type="l",
ylim=c(plot.PN.min-plot.PN.min*.1, plot.PN.max+plot.PN.max*.1),
xaxt = "n", main="a", lwd=2)
lines(rep(2,2),c(expert.P.BETA.results$ssBETA.mode.results$lower * lower.K,
expert.P.BETA.results$ssBETA.mode.results$upper * upper.K), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$lower * lower.K,2), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$upper * upper.K,2), lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$ssBETA.mode.results$mode * mode.K,2),col="red", lwd=2)
#plot new lower & upper conf
lines(c(1,2), rep(expert.P.BETA.results$feedbackBETA.mode.results$new.lower * new.lower,2)
, col="red" , lwd=2)
lines(c(1,2), rep(expert.P.BETA.results$feedbackBETA.mode.results$new.upper * new.upper,2)
, col="red", lwd=2)
#####
#GET RESULTS FOR OUTPUT
infl.lower=expert.P.BETA.results$ssBETA.mode.results$lower * lower.K
infl.upper=expert.P.BETA.results$ssBETA.mode.results$upper * upper.K
#store infl.best, based on which type is selected by expert - mode, mean or median
infl.best =expert.P.BETA.results$ssBETA.mode.results$mode * mode.K
infl.type="p"
###################################################
# IF 2
# EXPERT PROVIDES N OF UNKNOWN SPECIES
} else if (is.na(Best.N) =="FALSE") {
expert.N.noplot.ALLnorm.results <- expert.K.noplot.ALLnorm(Lower.N, Upper.N, Best.N, Sureness, new.alpha, best.type.status, ee.type=1)
#####
#GET RESULTS FOR OUTPUT
infl.lower=Lower.N
infl.upper=Upper.N
infl.best =Best.N
infl.type="n"
###
#store selected best type (mode, median, mean)
best.type <- expert.N.noplot.ALLnorm.results$best.type
#calculate K
Ksp <- expert.K.ALLnorm.results$Ksp
#calculate number of species for cryptic species
Cryp <- expert.N.noplot.ALLnorm.results$Ksp
#calculate number of species (including cryptic species
Nsp <- Ksp+Cryp
########
#PLOT
#NUMBER OF UNDISCOVER OR UNNAMED
####
#plot
par(mfrow=c(1,1), mar=c(1, 3.5, 4,.75)+.5, mgp=c(3, .75,0))
#MODE
##
if (expert.N.noplot.ALLnorm.results$ss.mode.results$lower <0) {
expert.N.noplot.ALLnorm.results$ss.mode.results$lower =0
}
if (expert.N.noplot.ALLnorm.results$feedback.mode.results$new.lower<0){
expert.N.noplot.ALLnorm.results$feedback.mode.results$new.lower =0
}
#find min & max for plot
plot.N.min <- min(
expert.N.noplot.ALLnorm.results$ss.mode.results$lower,
expert.N.noplot.ALLnorm.results$feedback.mode.results$new.lower)
plot.N.max <- max(
expert.N.noplot.ALLnorm.results$ss.mode.results$upper,
expert.N.noplot.ALLnorm.results$feedback.mode.results$new.upper)
##
#PLOT
plot(rep(c(1),2), rep(c(expert.N.noplot.ALLnorm.results$ss.mode.results$lower,
expert.N.noplot.ALLnorm.results$ss.mode.results$upper),1)
, xlim=c(-0.5,3), xlab="", ylab="", type="l",
ylim=c(plot.N.min-plot.N.min*.1, plot.N.max+plot.N.max*.1),
xaxt = "n", main="a",lwd=2)
lines(rep(2,2),c(expert.N.noplot.ALLnorm.results$ss.mode.results$lower,
expert.N.noplot.ALLnorm.results$ss.mode.results$upper),lwd=2)
lines(c(1,2), rep(expert.N.noplot.ALLnorm.results$ss.mode.results$lower,2),lwd=2)
lines(c(1,2), rep(expert.N.noplot.ALLnorm.results$ss.mode.results$upper,2),lwd=2)
lines(c(1,2), rep(expert.N.noplot.ALLnorm.results$ss.mode.results$mode,2),col="red",lwd=2)
lines(c(1,2), rep(Best.N,2),col=1, lwd=2) #best guess
legend("topleft", lty=1, col=c(1, "red"), bty="n", legend=c("elicited parameters",
"estimated (fitted) "),cex=.8) #BEST GUESS IS MODE
legend("topright", paste("new
sureness =", new.alpha), cex=.8, bty="n")
#plot new lower & upper conf
lines(c(1,2), rep(expert.N.noplot.ALLnorm.results$feedback.mode.results$new.lower,2), col="red" ,lwd=2)
lines(c(1,2), rep(expert.N.noplot.ALLnorm.results$feedback.mode.results$new.upper,2), col="red",lwd=2)
###################################################################
# IF 3
# EXPERT PROVIDES M MULTIPLICAB FACTOR
#e.g CRYPTIC SPP. IS X TIMES KNOWN SPECIES
} else {
expert.M.noplot.ALLnorm.results <- expert.K.noplot.ALLnorm(Lower.M, Upper.M, Best.M, Sureness, new.alpha, best.type.status, ee.type=3)
####
#PLOT
par(mfrow=c(2,1), mar=c(1, 3.5, 4,.75)+.5, mgp=c(3, .75,0))
if (expert.M.noplot.ALLnorm.results$ss.mode.results$lower <0) {
expert.M.noplot.ALLnorm.results$ss.mode.results$lower =0
}
if (expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower<0){
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower =0
}
###
#find min & max for plot
plot.M.min <- min(
expert.M.noplot.ALLnorm.results$ss.mode.results$lower,
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower)
plot.M.max <- max(
expert.M.noplot.ALLnorm.results$ss.mode.results$upper,
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.upper)
###
plot(rep(c(1),2), rep(c(expert.M.noplot.ALLnorm.results$ss.mode.results$lower,
expert.M.noplot.ALLnorm.results$ss.mode.results$upper),1)
, xlim=c(-0.5,3), xlab="", ylab="x", type="l",
ylim=c(plot.M.min-plot.M.min*.1, plot.M.max+plot.M.max*.1),
xaxt = "n", main="a",lwd=2)
lines(rep(2,2),c(expert.M.noplot.ALLnorm.results$ss.mode.results$lower,
expert.M.noplot.ALLnorm.results$ss.mode.results$upper),lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$lower,2),lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$upper,2),lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$mode,2),col="red",lwd=2)
lines(c(1,2), rep(Best.M,2),col=1, lwd=2) #best guess
legend("topleft", lty=1, col=c(1, "red"), bty="n", legend=c("elicited parameters",
"estimated (fitted) "),cex=.8) #BEST GUESS IS MODE
legend("topright", paste("new
sureness =", new.alpha), cex=.8, bty="n")
#plot new lower & upper conf
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower,2), col="red" ,lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$feedback.mode.results$new.upper,2), col="red",lwd=2)
###
# USE INFO FROM ELICITED KNOWN SPP. ( expert.K.ALLnorm.results)
#TO PLOT N (INFLATION (%) * K)
###
#pihat is returned from expert.K.LN function
pihat <- expert.K.ALLnorm.results$pihat
#which distribution was used in expert.K.ALLnorm.results
which.dist <- expert.K.ALLnorm.results$which.dist
MhatK <-expert.K.ALLnorm.results$MhatK
#get values of mu, sig, lower.K & upper.K
mu <- expert.K.ALLnorm.results$fit.best.mode.mu
sig <- expert.K.ALLnorm.results$fit.best.mode.sig
eval.text <- getURL("https://raw.githubusercontent.com/beckyfisher/ElicitN/master/feedback.AllNorm.R", ssl.verifypeer = FALSE)
eval(parse(text = eval.text))
#source("feedback.AllNorm.R")
feedback.AllNorm.results <- feedback.AllNorm(mu, sig,Sureness,new.alpha, MhatK, which.dist)
lower.K <- feedback.AllNorm.results$lower
upper.K <- feedback.AllNorm.results$upper
mode.K <- feedback.AllNorm.results$mode
new.lower <- feedback.AllNorm.results$new.lower
new.upper <- feedback.AllNorm.results$new.upper
###
if (expert.M.noplot.ALLnorm.results$ss.mode.results$lower<0) {
expert.M.noplot.ALLnorm.results$ss.mode.results$lower =0
}
if (lower.K<0) {
lower.K=0
}
if (expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower<0){
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower =0
}
#find min & max for plot
plot.PM.min <- min(
expert.M.noplot.ALLnorm.results$ss.mode.results$lower * lower.K,
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower * lower.K)
plot.PM.max <- max(
expert.M.noplot.ALLnorm.results$ss.mode.results$upper* upper.K,
expert.M.noplot.ALLnorm.results$feedback.mode.results$new.upper* new.upper)
plot(rep(c(1),2), rep(c(expert.M.noplot.ALLnorm.results$ss.mode.results$lower * lower.K,
expert.M.noplot.ALLnorm.results$ss.mode.results$upper * upper.K),1)
, xlim=c(-0.5,3), xlab="", ylab="N", type="l",
ylim=c(plot.PM.min-plot.PM.min*.1, plot.PM.max+plot.PM.max*.1),
xaxt = "n", main="a", lwd=2)
lines(rep(2,2),c(expert.M.noplot.ALLnorm.results$ss.mode.results$lower * lower.K,
expert.M.noplot.ALLnorm.results$ss.mode.results$upper * upper.K), lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$lower * lower.K,2), lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$upper * upper.K,2), lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$ss.mode.results$mode * mode.K,2),col="red", lwd=2)
#plot new lower & upper conf
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$feedback.mode.results$new.lower * new.lower,2)
, col="red" , lwd=2)
lines(c(1,2), rep(expert.M.noplot.ALLnorm.results$feedback.mode.results$new.upper * new.upper,2)
, col="red", lwd=2)
#####
#GET RESULTS FOR OUTPUT
infl.lower=expert.M.noplot.ALLnorm.results$ss.mode.results$lower * lower.K
infl.upper=expert.M.noplot.ALLnorm.results$ss.mode.results$upper * upper.K
infl.type="m"
#store infl.best, based on which type is selected by expert - mode, mean or median
infl.best =expert.M.noplot.ALLnorm.results$ss.mode.results$mode * mode.K
#calculate K
Ksp <- feedback.AllNorm.results$Ksp
#calculate number of species for cryptic species
Cryp <- expert.M.noplot.ALLnorm.results$Ksp
#############################################################
#END IF STATEMENT
}
output <- expert.K.noplot.ALLnorm(Lhat=infl.lower, Uhat=infl.upper, Mhat=infl.best, Sureness,
new.alpha, best.type.status, ee.type=1)
return(list(pihat=output$pihat,
ss.mode.results=output$ss.mode.results,
fit.best.mode.mu=output$fit.best.mode.mu,
fit.best.mode.sig=output$fit.best.mode.sig,
feedback.mode.results=output$feedback.mode.results,
Ksp=output$Ksp, MhatK=output$MhatK,which.dist=output$which.dist,
Cryp=Cryp))
}