diff --git a/R/run_simul.R b/R/run_simul.R
index 1f19b50640537aba4aa996c4b2d0aaf7373d9af6..3d86265964e8541a6dc304ab1b0edd5a86ba40fc 100644
--- a/R/run_simul.R
+++ b/R/run_simul.R
@@ -12,8 +12,8 @@
#' or the Number of Pairs ("Npair"). A stable age distribution is used to infer the size of each age class.
#' @param pop_growth_mean a number. Average population growth rate (lambda).
#' @param pop_growth_se Standard Error for population growth rate (= uncertainty around the value provided).
-#' @param survivals_mean a vector. Average survival probabilities for each age class.
-#' @param fecundities_mean a vector of fecundity values for each age class.
+#' @param survivals a vector. Average survival probabilities for each age class.
+#' @param fecundities a vector of fecundity values for each age class.
#' @param model_demo an R object corresponding to the demographic model to be used. The 4 possible models currently are:
#' M1_noDD_noDemoStoch, M2_noDD_WithDemoStoch, M3_WithDD_noDemoStoch, M4_WithDD_WithDemoStoch,
#' @param time_horzion a number. The number of years (time horizon) over which to project the population dynamics.
@@ -39,9 +39,9 @@
#' pop_growth_mean = 1
#' pop_growth_se = 0.03
#'
-#' survivals_mean <- c(0.5, 0.7, 0.8, 0.95)
+#' survivals <- c(0.5, 0.7, 0.8, 0.95)
#'
-#' fecundities_mean <- c(0, 0, 0.05, 0.55)
+#' fecundities <- c(0, 0, 0.05, 0.55)
#'
#' model_demo = M2_noDD_WithDemoStoch
#'
@@ -53,7 +53,7 @@
#' fatalities_mean, fatalities_se,
#' pop_size_mean, pop_size_se, pop_size_type,
#' pop_growth_mean, pop_growth_se,
-#' survivals_mean, fecundities_mean,
+#' survivals, fecundities,
#' model_demo, time_horzion, coeff_var_environ, fatal_constant)
#'
#'
@@ -61,7 +61,7 @@ run_simul <- function(nsim,
fatalities_mean, fatalities_se,
pop_size_mean, pop_size_se, pop_size_type,
pop_growth_mean, pop_growth_se,
- survivals_mean, fecundities_mean,
+ survivals, fecundities,
model_demo, time_horzion, coeff_var_environ, fatal_constant){
# Coefficient of variation for environment stochasticity
@@ -71,7 +71,7 @@ run_simul <- function(nsim,
nyr <- time_horzion
# Number of age classes
- nac <- length(survivals_mean)
+ nac <- length(survivals)
# Number of fatalities scenario (+1 because we include a base scenario of NO fatality)
nsc <- length(fatalities_mean)
@@ -97,7 +97,7 @@ run_simul <- function(nsim,
# 2. Population size : draw and distribute by age class
N0 <- sample_gamma(1, mu = pop_size_mean, sd = pop_size_se) %>%
round %>%
- pop_vector(pop_size_type = pop_size_type, s = survivals_mean, f = survivals_mean)
+ pop_vector(pop_size_type = pop_size_type, s = survivals, f = fecundities)
if(pop_growth_se > 0){
@@ -105,18 +105,18 @@ run_simul <- function(nsim,
lam0 <- sample_gamma(1, mu = pop_growth_mean, sd = pop_growth_se)
# 4. Calibrate vital rates to match the the desired lambda
- inits <- init_calib(s = survivals_mean, f = fecundities_mean, lam0 = lam0)
+ inits <- init_calib(s = survivals, f = fecundities, lam0 = lam0)
- vr <- calibrate_params(inits = inits, f = fecundities_mean, s = survivals_mean, lam0 = lam0)
- s <- head(vr, length(survivals_mean))
- f <- tail(vr, length(fecundities_mean))
+ 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))
}else{
# No parameter uncertainty on population growth
- s <- survivals_mean
- f <- fecundities_mean
+ s <- survivals
+ f <- fecundities
lam_it[sim] <- lambda(build_Leslie(s,f))
} # End if/else
diff --git a/devtools_history.R b/devtools_history.R
index 9f7b6699d0ff32dd37fffdb86f9be51c15c852ea..abe0f294e74a5c3ff81d95b7969859293987a856 100644
--- a/devtools_history.R
+++ b/devtools_history.R
@@ -36,3 +36,8 @@ usethis::use_package("dichromat")
# Ignore file "run_shiny.R"
usethis::use_build_ignore("run_shiny.R")
+
+
+## Put it on Git
+library(usethis)
+usethis::use_git()
diff --git a/man/run_simul.Rd b/man/run_simul.Rd
index bcb25aacce17a2ff785ebebafbdf52caa214ce3e..2f6402844bc298bbbea9ade4bb8484ba6d9b78fe 100644
--- a/man/run_simul.Rd
+++ b/man/run_simul.Rd
@@ -13,8 +13,8 @@ run_simul(
pop_size_type,
pop_growth_mean,
pop_growth_se,
- survivals_mean,
- fecundities_mean,
+ survivals,
+ fecundities,
model_demo,
time_horzion,
coeff_var_environ,
@@ -39,9 +39,9 @@ or the Number of Pairs ("Npair"). A stable age distribution is used to infer the
\item{pop_growth_se}{Standard Error for population growth rate (= uncertainty around the value provided).}
-\item{survivals_mean}{a vector. Average survival probabilities for each age class.}
+\item{survivals}{a vector. Average survival probabilities for each age class.}
-\item{fecundities_mean}{a vector of fecundity values for each age class.}
+\item{fecundities}{a vector of fecundity values for each age class.}
\item{model_demo}{an R object corresponding to the demographic model to be used. The 4 possible models currently are:
M1_noDD_noDemoStoch, M2_noDD_WithDemoStoch, M3_WithDD_noDemoStoch, M4_WithDD_WithDemoStoch,}
@@ -71,9 +71,9 @@ pop_size_type = "Npair"
pop_growth_mean = 1
pop_growth_se = 0.03
-survivals_mean <- c(0.5, 0.7, 0.8, 0.95)
+survivals <- c(0.5, 0.7, 0.8, 0.95)
-fecundities_mean <- c(0, 0, 0.05, 0.55)
+fecundities <- c(0, 0, 0.05, 0.55)
model_demo = M2_noDD_WithDemoStoch
@@ -85,7 +85,7 @@ run_simul(nsim = 10,
fatalities_mean, fatalities_se,
pop_size_mean, pop_size_se, pop_size_type,
pop_growth_mean, pop_growth_se,
- survivals_mean, fecundities_mean,
+ survivals, fecundities,
model_demo, time_horzion, coeff_var_environ, fatal_constant)