diff --git a/R/pop_project.R b/R/pop_project.R
index fb4a241f931aa72995f647059c8f60c3659f1f34..446e9a5a0c364d8ad18ca748b9f7507c73fd8728 100644
--- a/R/pop_project.R
+++ b/R/pop_project.R
@@ -72,9 +72,9 @@ pop_project <- function(fatalities,
# Fatalities : constant number (M) or constant rate (h)
if(fatal_constant == "M"){
- h <- M/apply(N[,t-1,], 2, sum)
+ h <- sapply(M/apply(N[,t-1,], 2, sum), min, 1)
} else {
- h <- M/apply(N[,1,], 2, sum)
+ h <- sapply(M/apply(N[,1,], 2, sum), min, 1)
}
# Sample a seed for RNG
diff --git a/R/pop_project_cumulated_impacts.R b/R/pop_project_cumulated_impacts.R
index b8e042f0d43931dfc5058e85a58d11e9cfdd138a..0dcf41dad707d80280ba28e9788089b920875fc0 100644
--- a/R/pop_project_cumulated_impacts.R
+++ b/R/pop_project_cumulated_impacts.R
@@ -83,9 +83,9 @@ pop_project_cumulated_impacts <- function(fatalities,
# Fatalities : constant number (M) or constant rate (h)
if(fatal_constant == "M"){
- h <- Mc[,t-1]/apply(N[,t-1,], 2, sum)
+ h <- sapply(Mc[,t-1]/apply(N[,t-1,], 2, sum), min, 1)
} else {
- h <- Mc[,t-1]/apply(N[,1,], 2, sum)
+ h <- sapply(Mc[,t-1]/apply(N[,1,], 2, sum), min, 1)
}
# Sample a seed for RNG
diff --git a/junk.R b/junk.R
index 7db12e710de03b49775eaa69542bbd6455e0772d..25bc685eca65c156690a4b24d7e77a0e7c069a66 100644
--- a/junk.R
+++ b/junk.R
@@ -1,46 +1,43 @@
rm(list = ls(all.names = TRUE))
graphics.off()
+
## Libraries
library(eolpop)
-library(magrittr)
-library(popbio)
-
-
-
-nsim = 10
-time_horzion = 60
-pop_growth_mean = 1.6
-
## Inputs
+nsim = 10
-fatalities_mean = c(0, 5)
-fatalities_se = c(0,0.05)
+fatalities_mean = c(0, 10, 5, 8)
+fatalities_se = c(0, 0.05, 0.05, 0.05)
pop_size_mean = 200
pop_size_se = 25
-pop_growth_se = 0.03
+pop_growth_mean = 1
+pop_growth_se = 0
survivals <- c(0.5, 0.7, 0.8, 0.95)
fecundities <- c(0, 0, 0.05, 0.55)
model_demo = NULL # M2_noDD_WithDemoStoch #M1_noDD_noDemoStoch #M4_WithDD_WithDemoStoch #M3_WithDD_noDemoStoch #
+time_horzion = 50
coeff_var_environ = 0.10
-fatal_constant = "h"
+fatal_constant = "M"
pop_size_type = "Npair"
-cumuated_impacts = FALSE
+cumuated_impacts = TRUE
onset_year = c(2010, 2013, 2016)
onset_time = onset_year - min(onset_year) + 1
+onset_time = c(min(onset_time), onset_time)
# Pop size total
sum(pop_vector(pop_size = pop_size_mean, pop_size_type = pop_size_type, s = survivals, f = fecundities))
+
# Define K
-carrying_capacity = 210
+carrying_capacity = 500
theta = 1
K = pop_vector(pop_size = carrying_capacity, pop_size_type = pop_size_type, s = survivals, f = fecundities) %>% sum
K
@@ -49,22 +46,11 @@ K
rMAX_species <- rMAX_spp(surv = tail(survivals,1), afr = min(which(fecundities != 0)))
rMAX_species
-
-
-
-##--------------------------------------------
-## Avoid dumb scenarios --
-##--------------------------------------------
+## Avoid unrealistic scenarios
pop_growth_mean <- min(1 + rMAX_species, pop_growth_mean)
pop_growth_mean
-
-
-
-
-#pop_growth_mean = 1 + rMAX_species
-
##--------------------------------------------
## Calibration --
##--------------------------------------------
@@ -126,7 +112,7 @@ sim=1
# 1. Nomber of fatalities
M <- NA
for(j in 1:nsc){
- M[j] <- sample_gamma(1, mu = fatalities_mean[j], sd = fatalities_se[j])
+ M[j] <- sample_gamma(1, mu = fatalities_mean[j], sd = fatalities_se[j])
}
# 2. Population size : draw and distribute by age class
@@ -208,54 +194,35 @@ sim=1
+ fatalities = M
+#######################################################################
+ ##--------------------------------------------
+ ## Pop project cumulated --
+ ##--------------------------------------------
+ ## Fatalities taking onset time into account
+ # Initiate matrix
+ Mi <- matrix(fatalities, nrow = length(fatalities), ncol = nyr)
+ # Fatalities from each wind farm
+ for(j in 2:nrow(Mi)){
+ if(onset_time[j] > 1) Mi[j,1:(onset_time[j]-1)] <- 0
+ } # j
-
-
-
-
-lam0 <- 1.5
-
-# 4. Calibrate vital rates to match the the desired lambda
-inits <- init_calib(s = survivals, f = fecundities, lam0 = lam0)
-
-vr <- calibrate_params(inits = inits, f = fecundities, s = survivals, lam0 = lam0)
-s <- head(vr, length(survivals))
-f <- tail(vr, length(fecundities))
-lam_it[sim] <- lambda(build_Leslie(s,f))
-lam_it[sim]
-lambda(build_Leslie(s,f))
-
-
-
-model_demo <- M3_WithDD_noDemoStoch
-DD_params$K <- 2500
-nyr = 60
-
-
-
-
-
-
-
-
-
-
-#######################################################################
- ##--------------------------------------------
- ## Pop project --
- ##--------------------------------------------
+ # Cumulated Fatalities
+ Mc <- Mi
+ for(j in 2:nrow(Mc)) Mc[j,] <- apply(Mc[(j-1):j,], 2, sum)
# Initiate Pop Size (output) Array
- N <- array(NA, dim = c(nac, nyr, nsc), dimnames = list(paste0("age", 1:nac),
+ N <- array(0, dim = c(nac, nyr, nsc), dimnames = list(paste0("age", 1:nac),
paste0("year", 1:nyr),
paste0("scenario", 1:nsc)
))
N[,1,] <- N0
+
## Loops over time (years)
for(t in 2:nyr){
@@ -265,14 +232,13 @@ nyr = 60
# Fatalities : constant number (M) or constant rate (h)
if(fatal_constant == "M"){
- h <- M/apply(N[,t-1,], 2, sum)
+ h <- Mc[,t-1]/apply(N[,t-1,], 2, sum)
} else {
- h <- M/apply(N[,1,], 2, sum)
+ h <- Mc[,t-1]/apply(N[,1,], 2, sum)
}
# Sample a seed for RNG
- seed <- ((((Sys.time() %>% as.numeric) %% 1e5) * 1e5) %% 1e5) %>% round
- #runif(1, 0, 1e6)
+ seed <- runif(1, 0, 1e6)
## Projection : apply the LESLIE matrix calculation forward
# Scenario 0
@@ -285,210 +251,34 @@ nyr = 60
-colSums(N[,,1])
-
-
-
-DD_params
-
-
-
-
-
-h=0
-N1 = N0
-sum(N1) * lambda(build_Leslie(s,f))
-K
-
-
-lam_Nt <- 1 + rMAX*(1-(sum(N1)/K)^theta)
-sum(N1) * lam_Nt
-
-
-A_Nt <- build_Leslie(s = s_Nt, f = f_Nt)
-lambda(A_Nt)
-
-N2 <- A_Nt%*%N1
-sum(N2)
-
-#######################################################################
-##--------------------------------------------
-## Model --
-##--------------------------------------------
-
-
-N1 = N2
-
-
-# Extract DD parameters from list
-rMAX = DD_params$rMAX
-K = DD_params$K
-theta = DD_params$theta
-
-# Apply density dependence effect
-lam_Nt <- 1 + rMAX*(1-(sum(N1)/K)^theta)
-
-# Calibrate vital rates to match lam_Nt
-A <- build_Leslie(s = s, f = f)
-diff_rel_lam <- (lam_Nt - lambda(A))/lambda(A)
-d <- match_lam_delta(diff_rel_lam = diff_rel_lam, s=s, f=f)
-vr_Nt <- c(s,f) + d
-
-s_Nt <- head(vr_Nt, length(s)) %>% sapply(min, 0.999)
-f_Nt <- tail(vr_Nt, length(f))
-
-## Check if approximation is close enough to desired lambda
-if( abs((lambda(build_Leslie(s = s_Nt, f = f_Nt)) - lam_Nt) / lam_Nt) > 0.005 ){
-
- # If difference is too large : Use optimisation function for better calibration
- inits <- c(tail(vr_Nt, length(f)), head(vr_Nt, length(s)) %>% sapply(min, 0.999))
- inits <- inits[inits != 0]
- vr_calib <- calibrate_params(inits = inits, f = f_Nt, s = s_Nt, lam0 = lam_Nt)
- s_Nt <- head(vr_calib, length(s_Nt))
- f_Nt <- tail(vr_calib, length(f_Nt))
-
-} # if
-
-
-# Number of age classes
-nac = length(s)
-
-# Survivors (to "natural mortality" (s) and Wind Turbine Fatalities (1-h))
-S <- rbinom(nac, N1, (1-h)*s_Nt)
-N2 <- c(rep(0, nac-1), tail(S,1)) + c(0, head(S,-1))
-
-# Births
-N2[1] <- sum(rpois(nac, f_Nt*N2))
-
-sum(N2)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-N1 = N2
-
- ## M3_WithDD_noDemoStoch
-
- # Extract DD parameters from list
- rMAX = DD_params$rMAX
- K = DD_params$K
- theta = DD_params$theta
-
- # Apply density dependence effect
- lam_Nt <- 1 + rMAX*(1-(sum(N1)/K)^theta)
-
- # Calibrate vital rates to match lam_Nt
- A <- build_Leslie(s = s, f = f)
- diff_rel_lam <- (lam_Nt - lambda(A))/lambda(A)
- d <- match_lam_delta(diff_rel_lam = diff_rel_lam, s=s, f=f)
- vr_Nt <- c(s,f) + d
-
- s_Nt <- head(vr_Nt, length(s)) %>% sapply(min, 0.999)
- f_Nt <- tail(vr_Nt, length(f))
-
- lambda(build_Leslie(s = s_Nt, f = f_Nt))
-
- ## Check if approximation is close enough to desired lambda
- if( abs((lambda(build_Leslie(s = s_Nt, f = f_Nt)) - lam_Nt) / lam_Nt) > 0.005 ){
-
- # If difference is too large : Use optimisation function for better calibration
- inits <- c(tail(vr_Nt, length(f)), head(vr_Nt, length(s)) %>% sapply(min, 0.999))
- inits <- inits[inits != 0]
- vr_calib <- calibrate_params(inits = inits, f = f_Nt, s = s_Nt, lam0 = lam_Nt)
- s_Nt <- head(vr_calib, length(s_Nt))
- f_Nt <- tail(vr_calib, length(f_Nt))
-
- } # if
-
- # Build the LESLIE matrix
- A_Nt <- build_Leslie(s = s_Nt, f = f_Nt)
-
- # Apply the LESLIE matrix calculation at t+1
- N2 <- A_Nt%*%N1*(1-h)
-
-sum(N2)
-
-
-
-
-
-
-
-
-
-
-
-
- build_Leslie(s = survivals, f = fecundities) %>% lambda
- N0 <- sample_gamma(1, mu = pop_size_mean, sd = pop_size_se) %>%
- round %>%
- pop_vector(pop_size_type = pop_size_type, s = survivals, f = fecundities)
-
- sum(N0)
- pop_size_mean + 3*pop_size_se
- carrying_capacity < (pop_size_mean + 3*pop_size_se)
-
-
-
- sum(N0)
- K
-
-
- lam0
- 1+rMAX_species
- pop_growth_mean
- DD_params
+ t = 31
+ j = 4
-##==============================================================================
-## Analyses (simulations) ==
-##==============================================================================
-run0 <- run_simul(nsim, cumuated_impacts,
- fatalities_mean, fatalities_se, onset_time,
- pop_size_mean, pop_size_se, pop_size_type,
- pop_growth_mean, pop_growth_se,
- survivals = s_calibrated, fecundities = f_calibrated,
- carrying_capacity = carrying_capacity, theta = theta,
- rMAX_species = rMAX_species,
- model_demo, time_horzion, coeff_var_environ, fatal_constant)
+ ## M2_noDD_WithDemoStoch
+ t = t+1
+ h = sapply(Mc[,t-1]/apply(N[,t-1,], 2, sum), min, 1)
+ h
-N = run0$N
-#plot_impact(N = N, xlab = "year", ylab = "pop size")
-plot_traj(N, xlab = "Annee", ylab = "Taille de population (totale)")
-abline(h = K)
+ #N1 = N[,t,j]
+ #N1 = N2
+ N1 = c(2,1,1,3)
+ # Number of age classes
+ nac = length(s)
+ # Survivors (to "natural mortality" (s) and Wind Turbine Fatalities (1-h))
+ S <- rbinom(nac, N1, (1-h)*s)
+ N2 <- c(rep(0, nac-1), tail(S,1)) + c(0, head(S,-1))
+ # Births
+ N2[1] <- sum(rpois(nac, f*N2))
-(N[,1,,1])
-colSums(N[,,,]) %>% max
+ N2
+ N[,t+1,j] = N2
diff --git a/run_analysis.R b/run_analysis.R
index 800c1995c56525e4596410ecc5049e96ddc09e2b..c9f40c8fc1735699dfa9782e96ecb64ac0582ee7 100644
--- a/run_analysis.R
+++ b/run_analysis.R
@@ -8,14 +8,14 @@ library(eolpop)
## Inputs
nsim = 10
-fatalities_mean = c(0, 5)
-fatalities_se = c(0,0.05)
+fatalities_mean = c(0, 10, 5, 8)
+fatalities_se = c(0, 0.05, 0.05, 0.05)
pop_size_mean = 200
pop_size_se = 25
-pop_growth_mean = 0.95
-pop_growth_se = 0.03
+pop_growth_mean = 1
+pop_growth_se = 0
survivals <- c(0.5, 0.7, 0.8, 0.95)
fecundities <- c(0, 0, 0.05, 0.55)
@@ -23,13 +23,14 @@ fecundities <- c(0, 0, 0.05, 0.55)
model_demo = NULL # M2_noDD_WithDemoStoch #M1_noDD_noDemoStoch #M4_WithDD_WithDemoStoch #M3_WithDD_noDemoStoch #
time_horzion = 50
coeff_var_environ = 0.10
-fatal_constant = "h"
-pop_size_type = "Ntotal"
+fatal_constant = "M"
+pop_size_type = "Npair"
-cumuated_impacts = FALSE
+cumuated_impacts = TRUE
onset_year = c(2010, 2013, 2016)
onset_time = onset_year - min(onset_year) + 1
+onset_time = c(min(onset_time), onset_time)
# Pop size total
sum(pop_vector(pop_size = pop_size_mean, pop_size_type = pop_size_type, s = survivals, f = fecundities))
@@ -50,8 +51,6 @@ pop_growth_mean <- min(1 + rMAX_species, pop_growth_mean)
pop_growth_mean
-
-
##--------------------------------------------
## Calibration --
##--------------------------------------------
@@ -66,7 +65,6 @@ build_Leslie(s = s_calibrated, f = f_calibrated) %>% lambda
-
##==============================================================================
## Analyses (simulations) ==
##==============================================================================
@@ -80,14 +78,17 @@ run0 <- run_simul(nsim, cumuated_impacts,
model_demo, time_horzion, coeff_var_environ, fatal_constant)
+
+
+
N <- run0$N ; dim(N)
plot_traj(N, xlab = "Annee", ylab = "Taille de population (totale)")
abline(h = K)
-
colSums(N[,,,]) %>% max
-# plot_impact(N, onset_year = onset_year , xlab = "Annee", ylab = "Impact relatif")
+plot_impact(N, onset_year = onset_year , xlab = "Annee", ylab = "Impact relatif")
+
#plot_impact(N = N, xlab = "year", ylab = "pop size")
#source("draws_histog.R")
#draws_histog(draws = run0$lambdas, mu = pop_growth_mean, se = pop_growth_se)