#Code will calculate matrices for fixed temperatures (Temp) and copepodid attachment rates (CopAttach). Mean values for R0, generation time, lambda, sensitivity, elasticity, and stable stage distributions


rm(list = ls()) #clear values in workspac ###WILL CLEAR EVERYTHING, BE CAREFUL IF YOU HAVE OTHER WORK OPEN###

#setwd() #set to your working directory. Be sure that sourced files (S6 and S8) are in the same directory
require(abind) #install if you do not have this

#Set parameters (Attachment rate and temperature to desired value)
CopAttach<-0.001
Temp=4

Iterations=998 #number of matrices calculated-2

source('S6_Sea_Lice_Parameters.R') #loads parameters 
source('S8_Sea_Lice_A.R') #calculates matrix

A=as.array(A)

L<-list(A)

source('S6_Sea_Lice_Parameters.R') #loads parameters 
source('S8_Sea_Lice_A.R') #calculates matrix

L<-c(L, list(A))
##Alist<-abind(Alist, A, rev.along=0)

for (i in 1:Iterations){
  
  source('S6_Sea_Lice_Parameters.R') #loads parameters 
  source('S8_Sea_Lice_A.R') #calculates matrix
  
  L<-c(L, list(A))
}


#Matrix quantiles
m<-mean(L)
mv<-var2(L)

#Elasticities
elastTimes<-lapply(L, elasticity)
em<-mean(elastTimes)#matrix of elasticity means
ev<-var2(elastTimes)#matrix of elasticity variances


#Sensitivity
SensiTimes<-lapply(L, sensitivity)
sm<-mean(SensiTimes) #matrix of sensitivity means
sv<-var2(SensiTimes) #matrix of sensitivity variances

#Lambda
y<-sapply(L, lambda)
mean(y)
quantile( y, c(0.025, .975) ) #95% confidence interval for lambda

##Histogram of Lambda
hist(y, br=30, col="palegreen", xlab="Lambda", main=paste('Estimates of Lambda at Temp=', Temp))
abline(v=quantile(y, c(0.025, .975)), lty=3)

#Generation times
y<-sapply(L, generation.time)
mean(y)
quantile( y, c(0.025, .975) ) #95% confidence interval

#Histogram of generation times
hist(y, br=30, col="palegreen", xlab="Generation Time", main=paste('Estimates of Generation Time at Temp=', Temp))
abline(v=quantile(y, c(0.025, .975)), lty=3)


#Net Reproductive Rate
y<-sapply(L, net.reproductive.rate)
mean(y)
quantile( y, c(0.025, .975) ) #95% confidence interval

#stable stage distribution
y<-sapply(L,stable.stage)
ssmean<-rowMeans(y)
low95<-rowQuantiles(y, probs=0.025)
upper95<-rowQuantiles(y, probs=0.975)
data.matrix(ssmean)
data.matrix(low95)
data.matrix(upper95)

#population projection (Will project population for 100 days. Population begins with 10 copepodids)
n<-(c(0, 10, 0, 0, 0, 0, 0))
p<-pop.projection(A,n,100)
stage.vector.plot(p$stage.vector[,1:100], xlab="Days", main=paste('Projection over time at temp =', Temp, ', larval attachment =', CopAttach), tck=0.01)