minor corrections on simul functions

R/models.R

@@ -133,9 +133,9 @@ M3_WithDD_noDemoStoch <- function(N1, s, f, h, DD_params){
   ## M3_WithDD_noDemoStoch
 
   # Extract DD parameters from list
-  rMAX = DD_params$rMAX
-  K = DD_params$K
-  theta = DD_params$theta
+  rMAX <- DD_params$rMAX
+  K <- DD_params$K
+  theta <- DD_params$theta
 
   # Apply density dependence effect
   lam_Nt <- 1 + rMAX*(1-(sum(N1)/K)^theta)
@@ -208,9 +208,9 @@ M4_WithDD_WithDemoStoch <- function(N1, s, f, h, DD_params){
   ## M4_WithDD_WithDemoStoch
 
   # Extract DD parameters from list
-  rMAX = DD_params$rMAX
-  K = DD_params$K
-  theta = DD_params$theta
+  rMAX <- DD_params$rMAX
+  K <- DD_params$K
+  theta <- DD_params$theta
 
   # Apply density dependence effect
   lam_Nt <- 1 + rMAX*(1-(sum(N1)/K)^theta)

man/run_simul.Rd

@@ -6,7 +6,7 @@
 \usage{
 run_simul(
   nsim,
-  cumuated_impacts,
+  cumulated_impacts,
   fatalities_mean,
   fatalities_se,
   onset_time,
@@ -29,7 +29,7 @@ run_simul(
 \arguments{
 \item{nsim}{number of simulation}
 
-\item{cumuated_impacts}{Logical. If TRUE, we used the projection model for cumulated impacts.}
+\item{cumulated_impacts}{Logical. If TRUE, we used the projection model for cumulated impacts.}
 
 \item{fatalities_mean}{a vector (numeric). Average number of fatalities, for each scenario.}
 
@@ -111,7 +111,7 @@ carrying_capacity = 1200
 theta = 1
 rMAX_species <- 0.15
 
-run_simul(nsim = 10, cumuated_impacts = FALSE,
+run_simul(nsim = 10, cumulated_impacts = FALSE,
            fatalities_mean, fatalities_se, onset_time = NULL,
            pop_size_mean, pop_size_se, pop_size_type,
            pop_growth_mean, pop_growth_se,

man/run_simul_shiny.Rd

@@ -8,7 +8,7 @@ the simulation progress bar in Shiny (incProgress function)}
 \usage{
 run_simul_shiny(
   nsim,
-  cumuated_impacts,
+  cumulated_impacts,
   fatalities_mean,
   fatalities_se,
   onset_time,
@@ -31,7 +31,7 @@ run_simul_shiny(
 \arguments{
 \item{nsim}{number of simulation}
 
-\item{cumuated_impacts}{Logical. If TRUE, we used the projection model for cumulated impacts.}
+\item{cumulated_impacts}{Logical. If TRUE, we used the projection model for cumulated impacts.}
 
 \item{fatalities_mean}{a vector (numeric). Average number of fatalities, for each scenario.}
 

run_analysis.R

 rm(list = ls(all.names = TRUE))
 graphics.off()
-
+library(popbio)
 
 ## Libraries
 library(eolpop)
@@ -14,7 +14,7 @@ fatalities_se = c(0, 0.05, 0.05, 0.05)
 pop_size_mean = 200
 pop_size_se = 25
 
-pop_growth_mean = 1
+pop_growth_mean = 1.1
 pop_growth_se = 0
 
 survivals <- c(0.5, 0.7, 0.8, 0.95)
@@ -23,8 +23,8 @@ 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 = "M"
-pop_size_type = "Npair"
+fatal_constant = "h"
+pop_size_type = "Ntotal"
 
 cumulated_impacts = TRUE
 
@@ -60,7 +60,7 @@ vr_calibrated <- calibrate_params(inits = inits, f = fecundities, s = survivals,
 s_calibrated <- head(vr_calibrated, length(survivals))
 f_calibrated <- tail(vr_calibrated, length(fecundities))
 
-build_Leslie(s = s_calibrated, f = f_calibrated) %>% lambda
+lambda( build_Leslie(s = s_calibrated, f = f_calibrated) )
 
 
 
@@ -84,16 +84,3 @@ names(run0)
 N <- run0$N ; dim(N)
 plot_traj(N, xlab = "Annee", ylab = "Taille de population (totale)")
 abline(h = K)
-
-colSums(N[,,,]) %>% max
-
-plot_impact(N, onset_year = onset_year , xlab = "Annee", ylab = "Impact relatif")
-
-
-N <- run0$N
-output <- get_metrics(N, cumuated_impacts = cumulated_impacts)
-output$scenario$Pext
-
-#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)