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##############################################
## Hide/Show : level 1
## Fatalities
output$hide_fatalities <- eventReactive({
input$button_fatalities
},{
if(input$button_fatalities%%2 == 1) TRUE else FALSE
}, ignoreInit = TRUE)
outputOptions(output, "hide_fatalities", suspendWhenHidden = FALSE)
## Population Size
output$hide_pop_size <- eventReactive({
input$button_pop_size
},{
if(input$button_pop_size%%2 == 1) TRUE else FALSE
}, ignoreInit = TRUE)
outputOptions(output, "hide_pop_size", suspendWhenHidden = FALSE)
## Population Growth
output$hide_pop_growth <- eventReactive({
input$button_pop_growth
},{
if(input$button_pop_growth%%2 == 1) TRUE else FALSE
}, ignoreInit = TRUE)
outputOptions(output, "hide_pop_growth", suspendWhenHidden = FALSE)
## Carrying capacity
output$hide_carrying_cap <- eventReactive({
input$button_carrying_cap
},{
if(input$button_carrying_cap%%2 == 1) TRUE else FALSE
}, ignoreInit = TRUE)
outputOptions(output, "hide_carrying_cap", suspendWhenHidden = FALSE)
# Display Carrying capacity Unit Info
output$carrying_cap_unit_info <- renderText({
if(input$pop_size_unit == "Npair"){
paste0("Nombre de couple")
} else {
paste0("Effectif total")
}
})
##############################################
## Hide/Show : level 2
#------------
# Hide all
#------------
shinyjs::hide("fatalities_mean")
shinyjs::hide("fatalities_se")
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shinyjs::hide("fatalities_lower")
shinyjs::hide("fatalities_upper")
shinyjs::hide("farm_number_cumulated")
shinyjs::hide("fatalities_mat_cumulated")
shinyjs::hide("pop_size_lower")
shinyjs::hide("pop_size_upper")
shinyjs::hide("pop_size_mean")
shinyjs::hide("pop_size_se")
shinyjs::hide("pop_size_mat_expert")
shinyjs::hide("pop_growth_lower")
shinyjs::hide("pop_growth_upper")
shinyjs::hide("pop_growth_mean")
shinyjs::hide("pop_growth_se")
shinyjs::hide("pop_growth_mat_expert")
shinyjs::hide("pop_trend")
shinyjs::hide("pop_trend_strength")
shinyjs::hide("carrying_capacity")
shinyjs::hide("carrying_cap_mat_expert")
shinyjs::hide("carrying_cap_run_expert")
#------------
# Show some
#------------
# Show inputs for fatalities part
# Show inputs for none cumulated impacts scenario
if(input$analysis_choice == "scenario"){
shinyjs::show("fatalities_input_type")
if(input$fatalities_input_type == "itvl"){
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shinyjs::show("fatalities_lower")
shinyjs::show("fatalities_upper")
if(input$fatalities_input_type == "val"){
shinyjs::show("fatalities_mean")
shinyjs::show("fatalities_se")
}
}
}
# Show inputs for cumulated scenario
if(input$analysis_choice == "cumulated"){
shinyjs::hide("fatalities_input_type")
shinyjs::show("farm_number_cumulated")
shinyjs::show("fatalities_mat_cumulated")
# Show inputs for population size part
if(input$button_pop_size%%2 == 1){
shinyjs::show("pop_size_input_type")
if(input$pop_size_input_type == "itvl"){
shinyjs::show("pop_size_lower")
shinyjs::show("pop_size_upper")
}
shinyjs::show("pop_size_mean")
shinyjs::show("pop_size_se")
}
# Show inputs for population trend/growth part
if(input$button_pop_growth%%2 == 1){
shinyjs::show("pop_growth_input_type")
if(input$pop_growth_input_type == "itvl"){
shinyjs::show("pop_growth_lower")
shinyjs::show("pop_growth_upper")
}
shinyjs::show("pop_growth_mean")
shinyjs::show("pop_growth_se")
}
if(input$pop_trend != "stable"){
shinyjs::show("pop_trend_strength")
}
# Show inputs for carrying capacity part
if(input$button_carrying_cap%%2 == 1){
shinyjs::show("carrying_cap_input_type")
if(input$carrying_cap_input_type == "val"){
shinyjs::show("carrying_capacity")
}
if(input$carrying_cap_input_type == "eli_exp"){
shinyjs::show("carrying_cap_mat_expert")
shinyjs::show("carrying_cap_run_expert")
}
}
# Show inputs vital rates part
}) # en observe show/hide
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##############################################
## Reactive values
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out <- reactiveValues(run = NULL, msg = NULL)
ready <- reactiveValues(fatalities = TRUE, pop_size = TRUE, pop_growth = TRUE, carrying_capacity = TRUE)
nsim = NULL,
cumulated_impacts = FALSE,
fatalities_se = NULL,
onset_time = NULL,
onset_year = NULL,
pop_size_mean = NULL,
pop_size_se = NULL,
pop_growth_mean = NULL,
pop_growth_se = NULL,
fecundities = NULL,
survivals = NULL,
s_calibrated = NULL,
f_calibrated = NULL,
vr_calibrated = NULL,
theta = NULL,
model_demo = NULL,
time_horzion = NULL,
coeff_var_environ = NULL,
fatal_constant = NULL,
fatalities_eli_result = NULL,
pop_size_eli_result = NULL,
pop_growth_eli_result = NULL,
carrying_cap_eli_result = NULL
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##############################################
## Update matrix cumulated impact
##-------------------------------------------
observeEvent({
input$farm_number_cumulated
}, {
nfarm <- input$farm_number_cumulated
init_cumul_new <- init_cumul[1:nfarm,]
updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
value = matrix(init_cumul_new, nrow = nfarm, ncol = 3, byrow = FALSE,
dimnames = list(paste("Parc", c(1:nfarm)),
c("Moyenne",
"Erreur-type",
#####
##--------------------------------------------
##--------------------------------------------
# Function to run the elication analysis
func_eli <- function(mat_expert){
t_mat_expert <- t(mat_expert)
vals <- t_mat_expert[2:4,]
Cp <- t_mat_expert[5,]
weights <- t_mat_expert[1,]
out <- elicitation(vals, Cp, weights)
return(list(out = out, mean = out$mean_smooth, SE = sqrt(out$var_smooth)))
}
# Function to plot the elication analysis output
plot_expert <- function(out, show_se = TRUE, ...){
plot_elicitation(out, ylab = "", xlab = "Valeur du paramtre", cex.lab = 1.2, yaxt = "n")
mtext(text = "Densit de probabilit", side = 2, line = 2, cex = 1.2)
y2 <- dgamma(x = out$mean_smooth, shape = out$shape_smooth, rate = out$rate_smooth)
xx <- qgamma(p = c(0.01,0.99), shape = out$shape_smooth, rate = out$rate_smooth)
clip(xx[1], xx[2], -100, y2)
abline(v = out$mean_smooth, lwd = 3, col = "darkblue")
mtext(text = paste("Moyenne = ", round(out$mean_smooth,2)), side = 3, line = 2.5, cex = 1.2, adj = 0)
if(show_se) mtext(text = paste("Erreur-type = ", round(sqrt(out$var_smooth), 2)), side = 3, line = 1, cex = 1.2, adj = 0)
}
## Fatalities
##----------------------
observeEvent({
input$fatalities_run_expert
}, {
if( all(!is.na(input$fatalities_mat_expert)) ) {
param$fatalities_eli_result <- func_eli(input$fatalities_mat_expert)
output$title_distri_plot <- renderText({ "Mortalits annuelles" })
output$distri_plot <- renderPlot({ plot_expert(param$fatalities_eli_result$out) })
} else {
print("missing value")
} # end if
}) # end observeEvent
## Population size
##----------------------
observeEvent({
input$pop_size_run_expert
}, {
if(all(!is.na(input$pop_size_mat_expert))) {
## run elicitation analysis
param$pop_size_eli_result <- func_eli(input$pop_size_mat_expert)
output$title_distri_plot <- renderText({ "Taille de population" })
output$distri_plot <- renderPlot({ plot_expert(param$pop_size_eli_result$out) })
} else {
print("missing value")
} # end if
}) # end observeEvent
## Population growth
##----------------------
observeEvent({
input$pop_growth_run_expert
},{
if(all(!is.na(input$pop_growth_mat_expert))) {
## run elicitation analysis
param$pop_growth_eli_result <- func_eli(input$pop_growth_mat_expert)
output$title_distri_plot <- renderText({ "Taux de croissance de la population" })
output$distri_plot <- renderPlot({ plot_expert(param$pop_growth_eli_result$out) })
} else {
print("missing value")
} # end if
}) # end observeEvent
## Carrying capacity
##----------------------
observeEvent({
input$carrying_cap_run_expert
},{
if(all(!is.na(input$carrying_cap_mat_expert))) {
## run elicitation analysis
param$carrying_cap_eli_result <- func_eli(input$carrying_cap_mat_expert)
output$title_distri_plot <- renderText({ "Capacit de charge" })
output$distri_plot <- renderPlot({ plot_expert(param$carrying_cap_eli_result$out, show_se = FALSE) })
} else {
print("missing value")
} # end if
}) # end observeEvent
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
#####
#####
##--------------------------------------------
## Display parameter distribution
##--------------------------------------------
# Function to plot a gamma distribution
plot_gamma <- function(mu, se, show_mode = TRUE, show_mean = TRUE, show_se = TRUE, ...){
## Define shape and scale parameter of gamma distribution
shape = (mu/se)^2
scale = se^2/mu
par(mar = c(5, 4, 6, 2))
curve(dgamma(x, shape=shape, scale=scale), from = max(0,mu-3*se), to = mu+4*se, lwd = 3, col = "darkblue", yaxt = "n",
ylab = "", xlab = "Valeur du paramtre", cex.lab = 1.2)
mtext(text = "Densit de probabilit", side = 2, line = 2, cex = 1.2)
# show mode
MU <- (shape-1)*scale
y_MU <- dgamma(x = MU, shape = shape, scale = scale)
xx <- qgamma(p = c(0.01,0.99), shape = shape, scale = scale)
clip(xx[1], xx[2], -100, y_MU)
abline(v = MU, lwd = 3, col = "darkblue")
# show mean
y_mu <- dgamma(x = mu, shape = shape, scale = scale)
clip(xx[1], xx[2], -100, y_mu)
abline(v = mu, lwd = 2, col = "darkblue", lty = 2)
if(show_mode) mtext(text = paste("Mode = ", round(MU, 1)), side = 3, line = 4, cex = 1.2, adj = 0)
if(show_mean) mtext(text = paste("Moyenne = ", round(mu, 1)), side = 3, line = 2.5, cex = 1.2, adj = 0)
if(show_se) mtext(text = paste("Erreur-type = ", round(se, 2)), side = 3, line = 1, cex = 1.2, adj = 0)
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} # end function plot_gamma
plot_gamma_cumulated_impacts <- function(mu, se, nparc, ...){
## Define shape and scale parameter of gamma distribution
shape = (mu/se)^2
scale = se^2/mu
## Define x and y lim
xx = yy = list()
for(j in 1:nparc){
xx[[j]] = seq(from = max(0,mu[j]-4*se[j]), to = mu[j]+4*se[j], length.out = 1e3)
yy[[j]] = dgamma(xx[[j]], shape=shape[j], scale=scale[j])
}
ylim = c(min(unlist(yy)), max(unlist(yy))*1.4)
xlim = c(min(unlist(xx)), max(unlist(xx)))
## Plot
j=1
curve(dgamma(x, shape=shape[j], scale=scale[j]),
from = max(0,mu[j]-4*se[j]), to = mu[j]+4*se[j], n = 1e4,
xlim = xlim, ylim = ylim,
lwd = 3, col = j, yaxt = "n", xaxt = "n",
#xaxp = c(round(xlim[1]), round(xlim[2]), n = 10),
ylab = "", xlab = "Valeur du paramtre", cex.lab = 1.2)
axis(side = 1, at = seq(round(xlim[1]), round(xlim[2]),
by = max(round((round(xlim[2])-round(xlim[1]))/10),1) ))
mtext(text = "Densit de probabilit", side = 2, line = 2, cex = 1.2)
y1 <- dgamma(x = mu[j], shape = shape[j], scale = scale[j])
segments(x0 = mu[j], y0 = 0, y1 = y1, lty = 2, lwd = 3, col = j)
points(x = mu[j], y = y1, pch = 19, cex = 1.5, col = j)
for(j in 2:nparc){
curve(dgamma(x, shape=shape[j], scale=scale[j]),
from = max(0,mu[j]-4*se[j]), to = mu[j]+4*se[j], n = 1e4,
lwd = 3, col = j, yaxt = "n",
ylab = "", xlab = "Valeur du paramtre", cex.lab = 1.2, add = TRUE)
y1 <- dgamma(x = mu[j], shape = shape[j], scale = scale[j])
segments(x0 = mu[j], y0 = 0, y1 = y1, lty = 2, lwd = 3, col = j)
points(x = mu[j], y = y1, pch = 19, cex = 1.5, col = j)
}
legend(x = xlim[1], y = ylim[2], legend = paste("Parc", 1:nparc),
lwd = 3, col = 1:nparc, text.col = 1:nparc, cex = 1.5,
bty = "n", horiz = TRUE)
} # end function plot_gamma_cumulated_impacts
########################
## Fatalities
##----------------------
observeEvent({
input$analysis_choice
input$button_fatalities
input$fatalities_input_type
input$fatalities_run_expert
input$farm_number_cumulated
input$fatalities_mat_cumulated
},{
if(input$analysis_choice != "cumulated"){
# Show from input values: if button is ON and input_type is set on "value" or "itvl" (thus not "eli_exp")
if(input$button_fatalities%%2 == 1 & input$fatalities_input_type != "eli_exp"){
output$title_distri_plot <- renderText({ "Mortalits annuelles" })
output$distri_plot <- renderPlot({
if(input$fatalities_input_type == "itvl"){
req(input$fatalities_lower, input$fatalities_upper)
plot_gamma(mu = tail(param$fatalities_mean, -1), se = tail(param$fatalities_se, -1))
}else{
req(input$fatalities_mean, input$fatalities_se)
plot_gamma(mu = tail(param$fatalities_mean, -1), se = tail(param$fatalities_se, -1))
}
})
} else {
# Show from elicitation expert: if button is ON and input_type is set on "expert elicitation"
if(input$button_fatalities%%2 == 1 & input$fatalities_input_type == "eli_exp"){
if(!is.null(param$fatalities_eli_result)){
output$title_distri_plot <- renderText({ "Mortalits annuelles" })
output$distri_plot <- renderPlot({ plot_expert(param$fatalities_eli_result$out) })
} else {
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
# Hide otherwise (when button is OFF)
}else{
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
}
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# When analysis = cumulated impacts
}else{
output$title_distri_plot <- renderText({ "Mortalits annuelles par parc (impacts cumuls)" })
# Plot: note we use the "NULL + delay" sequence only to avoid error message in R console
output$distri_plot <- NULL
delay(5,
output$distri_plot <- renderPlot({
plot_gamma_cumulated_impacts(mu = input$fatalities_mat_cumulated[,1],
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se = input$fatalities_mat_cumulated[,2],
nparc = input$farm_number_cumulated)
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} # end "if"
########################
## Population size
##----------------------
observeEvent({
input$button_pop_size
},{
# Show from input values: if button is ON and input_type is set on "value"
if(input$button_pop_size%%2 == 1 & input$pop_size_input_type != "eli_exp"){
output$title_distri_plot <- renderText({ "Taille initiale de la population" })
output$distri_plot <- renderPlot({
req(param$pop_size_mean, param$pop_size_se)
plot_gamma(mu = param$pop_size_mean, se = param$pop_size_se)
})
} else {
# Show from elicitation expert: if button is ON and input_type is set on "expert elicitation"
if(input$button_pop_size%%2 == 1 & input$pop_size_input_type == "eli_exp"){
if(!is.null(param$pop_size_eli_result)){
output$title_distri_plot <- renderText({ "Taille initiale de la population" })
output$distri_plot <- renderPlot({ plot_expert(param$pop_size_eli_result$out) })
} else {
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
# Hide otherwise (when button is OFF)
}else{
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
########################
## Population growth
##----------------------
observeEvent({
input$pop_growth_input_type
input$button_pop_growth
},{
# Show from input values: if button is ON and input_type is set on "value" or "interval"
if(input$button_pop_growth%%2 == 1 & input$pop_growth_input_type != "eli_exp" & input$pop_growth_input_type != "trend"){
output$title_distri_plot <- renderText({ "Taux de croissance de la population" })
output$distri_plot <- renderPlot({
req(param$pop_growth_mean, param$pop_growth_se > 0)
plot_gamma(mu = param$pop_growth_mean, se = param$pop_growth_se)
})
} else {
# Show from elicitation expert: if button is ON and input_type is set on "expert elicitation"
if(input$button_pop_growth%%2 == 1 & input$pop_growth_input_type == "eli_exp"){
if(!is.null(param$pop_growth_eli_result)){
output$title_distri_plot <- renderText({ "Taux de croissance de la population" })
output$distri_plot <- renderPlot({ plot_expert(param$pop_growth_eli_result$out) })
} else {
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
# Hide otherwise (when button is OFF)
}else{
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
}
}, ignoreInit = FALSE)
########################
## Carrying capacity
##----------------------
observeEvent({
input$carrying_cap_input_type
input$button_carrying_cap
},{
# Show from elicitation expert: if button is ON and input_type is set on "expert elicitation"
if(input$button_carrying_cap%%2 == 1 & input$carrying_cap_input_type == "eli_exp"){
if(!is.null(param$carrying_cap_eli_result)){
output$title_distri_plot <- renderText({ "Capacit de charge" })
output$distri_plot <- renderPlot({ plot_expert(param$carrying_cap_eli_result$out) })
} else {
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
# Hide otherwise (when button is OFF)
}else{
output$title_distri_plot <- NULL
output$distri_plot <- NULL
}
}, ignoreInit = FALSE)
#####
#####
##-------------------------------------------------
## Display parameter values (on the side panel)
##-------------------------------------------------
#################################
##-------------------------------
output$fatalities_mean_info <- renderText({
paste0(c("Moyenne : ",
paste0(c(tail(param$fatalities_mean, -1)), collapse = ", ")
), collapse = "")
})
output$fatalities_se_info <- renderText({
paste0(c("Erreur-type : ",
paste0(c(tail(param$fatalities_se, -1)), collapse = ", ")
), collapse = "")
})
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#################################
## Poplutation size
##-------------------------------
## UNIT
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output$pop_size_unit_info <- renderText({
if(!is.null(param$pop_size_unit)){
if(param$pop_size_unit == "Npair"){
paste0("Nombre de couple")
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paste0("Effectif total")
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output$pop_size_mean_info <- renderText({ paste0("Moyenne : ", param$pop_size_mean) })
output$pop_size_se_info <- renderText({ paste0("Erreur-type : ", param$pop_size_se) })
## Show Popsize by age (table)
# Function to create the table
make_mat_popsizes <- function(data_sf, species, pop_size, pop_size_unit, survivals, fecundities){
nam <- data_sf %>%
filter(Nom_espece == species) %>%
select(classes_age) %>%
unlist %>%
as.vector
matrix(round(pop_vector(pop_size = pop_size, pop_size_type = pop_size_unit, s = survivals, f = fecundities)),
nrow = 1,
dimnames = list("Effectifs", nam)
)
}
# Display the table (Note the delay : piece is just there to avoid an error message - time for parameters to be "loaded in")
delay(200,
output$pop_size_by_age <- renderTable({
if(any(is.na(param$survivals)) | any(is.na(param$fecundities))){
matrix("Valeurs de survies et/ ou de fcondits manquantes",
nrow = 1, dimnames = list(NULL, "Erreur"))
}else{
make_mat_popsizes(data_sf = data_sf, species = input$species_choice, pop_size = param$pop_size_mean,
pop_size_unit = input$pop_size_unit, s = param$survivals, f = param$fecundities)
} # end if
},
width = "500px",
rownames = FALSE,
digits = 0)
)
#################################
## Population growth
##-------------------------------
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output$pop_growth_mean_info <- renderText({ paste0("Moyenne : ", param$pop_growth_mean) })
output$pop_growth_se_info <- renderText({ paste0("Erreur-type : ", param$pop_growth_se) })
#################################
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## Carrying capacity
##-------------------------------
# UNIT (like pop size)
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# Source info "unit"
if(is.null(param$pop_size_unit)){
unit1 <- input$pop_size_unit
}else{
unit1 <- param$pop_size_unit
}
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if(unit1 == "Npair"){
info1 <- paste0("Nombre de couple")
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info1 <- paste0("Effectif total")
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paste0(info1, " : ", param$carrying_capacity)
#################################
##-------------------------------
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# Function to create the matrix
make_mat_vr <- function(data_sf, species){
out_mat <- data_sf %>%
filter(Nom_espece == species) %>%
select(classes_age, survie, fecondite)
return(out_mat)
}
# Update the vital rate matrix (mat_fill_vr) when changing species in the list
observeEvent({
input$species_choice
}, {
if(input$species_choice == "Espce gnrique") {} else {
tab_species <- make_mat_vr(data_sf = data_sf, species = input$species_choice)
if(all(is.na(tab_species))) {
updateMatrixInput(session, inputId = "mat_fill_vr",
value = matrix(data = NA,
nrow = 4,
ncol = 2,
dimnames = list(c("Juv 0", "Sub 1", "Sub 2", "Adulte"), c("Survie", "Fcondit"))))
} else {
number_age_class <- nrow(tab_species)
ages <- tab_species$classes_age
survivals <- tab_species$survie
fecundities <- tab_species$fecondite
updateMatrixInput(session, inputId = "mat_fill_vr",
value = matrix(data = c(survivals, fecundities),
nrow = number_age_class,
ncol = 2,
dimnames = list(ages, c("Survie", "Fcondit"))))
} # end if 2
} # end if 1
}) # end observeEvent species_list
# Display vital rates output table
# Display intrinsic lambda (based solely on Leslie matrix)
output$lambda0_info <- renderUI({
lam <- lambda(build_Leslie(s = input$mat_fill_vr[,1], f = input$mat_fill_vr[,2]))
withMathJax(sprintf("$$\\lambda = %.02f$$", lam))
})
#####
##--------------------------------------------
## Select parameter values for simulations
##--------------------------------------------
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# Functions to calculate mean and SD from lower & upper values
get_mu <- function(lower, upper) (lower + upper)/2
get_sd <- function(lower, upper, coverage) ((abs(upper - lower)/2))/qnorm(1-((1-coverage)/2))
#################################
## Cumulated impacts or not ?
##-------------------------------
observeEvent({
input$run
}, {
if(input$analysis_choice == "scenario"){
param$cumulated_impacts = FALSE
} else {
param$cumulated_impacts = TRUE
} # end if
}) # end observeEvent
#################################
## Fatalities
##-------------------------------
observe({
# Case 1 : Not cumulated effects (if1)
if(input$analysis_choice == "scenario"){
# Case 1.1 : Values from expert elicitation (if2)
if(input$fatalities_input_type == "eli_exp"){
if(!(is.null(param$fatalities_eli_result))){
param$fatalities_mean <- c(0, round(param$fatalities_eli_result$mean, 2))
param$onset_time <- NULL
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param$fatalities_se <- c(0, round(param$fatalities_eli_result$SE, 3))
ready$fatalities <- TRUE
} else {
ready$fatalities <- FALSE
}
} else {
if(input$fatalities_input_type == "val"){
# Case 1.2 : Values directly provided as mean & SE
param$fatalities_mean <- c(0, input$fatalities_mean)
param$onset_time <- NULL
param$fatalities_se <- c(0, input$fatalities_se)
ready$fatalities <- TRUE
}else{
# Case 1.3 : Values directly provided as lower/upper interval
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param$fatalities_mean <- c(0, round(get_mu(lower = input$fatalities_lower, upper = input$fatalities_upper), 2))
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param$fatalities_se <- c(0, round(get_sd(lower = input$fatalities_lower, upper = input$fatalities_upper, coverage = CP), 3))
} # end (if2)
# Case 2 : Cumulated effects (if-else 1)
} else {
ready$fatalities <- TRUE
param$fatalities_mean <- c(0, input$fatalities_mat_cumulated[,1])
param$fatalities_se <- c(0, input$fatalities_mat_cumulated[,2])
param$onset_year <- c(min(input$fatalities_mat_cumulated[,3]), input$fatalities_mat_cumulated[,3])
param$onset_time <- param$onset_year - min(param$onset_year) + 1
} # end (if1)
}) # end observe
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
#################################
## Population size
##-------------------------------
observe({
# Case 1 : Values from expert elicitation
if(input$pop_size_input_type == "eli_exp"){
if(!(is.null(param$pop_size_eli_result))){
param$pop_size_mean <- round(param$pop_size_eli_result$mean)
param$pop_size_se <- round(param$pop_size_eli_result$SE)
ready$pop_size <- TRUE
} else {
ready$pop_size <- FALSE
}
} else {
if(input$pop_size_input_type == "val"){
# Case 2 : Values directly provided as mean & SE
ready$pop_size <- TRUE
param$pop_size_mean <- input$pop_size_mean
param$pop_size_se <- input$pop_size_se
}else{
# Case 3 : Values directly provided as lower/upper interval
ready$pop_size <- TRUE
param$pop_size_mean <- round(get_mu(lower = input$pop_size_lower, upper = input$pop_size_upper), 2)
param$pop_size_se <- round(get_sd(lower = input$pop_size_lower, upper = input$pop_size_upper, coverage = CP), 3)
} # end (if3)
}
param$pop_size_unit <- input$pop_size_unit
})
#################################
## Population growth
##-------------------------------
observe({
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# Case 1 : Values from expert elicitation
if(input$pop_growth_input_type == "eli_exp"){
if(!(is.null(param$pop_growth_eli_result))){
param$pop_growth_mean <- round(min(1 + param$rMAX_species, round(param$pop_growth_eli_result$mean, 2)), 2)
param$pop_growth_se <- round(param$pop_growth_eli_result$SE, 2)
ready$pop_growth <- TRUE
} else {
ready$pop_growth <- FALSE
}
} else {
# Case 2 : Trend information
if(input$pop_growth_input_type == "trend"){
ready$pop_growth <- TRUE
if(input$pop_trend == "growth") {
if(input$pop_trend_strength == "weak") {
param$pop_growth_mean <- 1.01
} else if(input$pop_trend_strength == "average"){
param$pop_growth_mean <- 1.03
} else {
param$pop_growth_mean <- 1.06
}
} else if(input$pop_trend == "decline"){
if(input$pop_trend_strength == "weak") {
param$pop_growth_mean <- 0.99
} else if(input$pop_trend_strength == "average"){
param$pop_growth_mean <- 0.97
} else {
param$pop_growth_mean <- 0.94
}
} else {
param$pop_growth_mean <- 1
}
param$pop_growth_se <- 0
# Case 3 : Values directly provided (i.e., not from expert elicitation)
} else {
if(input$pop_growth_input_type == "val"){
# Case 2 : Values directly provided as mean & SE
ready$pop_growth <- TRUE
param$pop_growth_mean <- round(min(1 + param$rMAX_species, input$pop_growth_mean), 3)
param$pop_growth_se <- input$pop_growth_se
}else{
# Case 3 : Values directly provided as lower/upper interval
ready$pop_growth <- TRUE
param$pop_growth_mean <- round(min(1 + param$rMAX_species,
round(get_mu(lower = input$pop_growth_lower, upper = input$pop_growth_upper), 2)
), 3)
param$pop_growth_se <- round(get_sd(lower = input$pop_growth_lower, upper = input$pop_growth_upper, coverage = CP), 3)
} # end (if3)
}
}
})
#################################
## Carrying capacity
##------------------------------
observe({
if(input$carrying_cap_input_type == "eli_exp"){
if(!(is.null(param$carrying_cap_eli_result))){
param$carrying_capacity <- round(param$carrying_cap_eli_result$mean)
ready$carrying_capacity <- TRUE
} else {
ready$carrying_capacity <- FALSE
}
} else {
ready$carrying_capacity <- TRUE
param$carrying_capacity <- input$carrying_capacity
}
})
#############################################
## Survivals, fecundities and rMAX_species
##-------------------------------------------
param$survivals <- input$mat_fill_vr[,1]
param$fecundities <- input$mat_fill_vr[,2]
}) # end observeEvent
#####
#############################################
## Calibration of survivals & fecundities
##-------------------------------------------
observeEvent({
input$run
},{
# we also define rMAX here
param$rMAX_species <- rMAX_spp(surv = tail(param$survivals,1), afr = min(which(param$fecundities != 0)))
param$vr_calibrated <- calibrate_params(
inits = init_calib(s = param$survivals, f = param$fecundities, lam0 = param$pop_growth_mean),
f = param$fecundities, s = param$survivals, lam0 = param$pop_growth_mean
)
param$s_calibrated <- head(param$vr_calibrated, length(param$survivals))
param$f_calibrated <- tail(param$vr_calibrated, length(param$fecundities))
})
#####
############################################################
## Observe parameter values to be used in simulations run
##----------------------------------------------------------
observe({
param # required to ensure up-to-date values are run
# simple inputs
param$nsim <- input$nsim