Newer
Older
##############################################
## 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")
thierrychambert
committed
shinyjs::hide("fatalities_lower")
shinyjs::hide("fatalities_upper")
shinyjs::hide("fatalities_number_expert")
shinyjs::hide("farm_number_cumulated")
shinyjs::hide("fatalities_mat_cumulated")
shinyjs::hide("fatalities_vec_scenario")
shinyjs::hide("pop_size_lower")
shinyjs::hide("pop_size_upper")
shinyjs::hide("pop_size_mean")
shinyjs::hide("pop_size_se")
shinyjs::hide("pop_size_number_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_number_expert")
shinyjs::hide("pop_trend")
shinyjs::hide("pop_trend_strength")
shinyjs::hide("carrying_capacity_lower")
shinyjs::hide("carrying_capacity_upper")
shinyjs::hide("carrying_capacity_mean")
shinyjs::hide("carrying_capacity_se")
shinyjs::hide("carrying_cap_number_expert")
shinyjs::hide("carrying_cap_mat_expert")
shinyjs::hide("carrying_cap_run_expert")
#------------
# Show some
#------------
# Show inputs for fatalities part
# Show inputs for single farm option (non-cumulated impacts)
if(input$analysis_choice == "single_farm"){
if(input$fatalities_input_type == "itvl"){
thierrychambert
committed
shinyjs::show("fatalities_lower")
shinyjs::show("fatalities_upper")
if(input$fatalities_input_type == "val"){
shinyjs::show("fatalities_mean")
shinyjs::show("fatalities_se")
}
shinyjs::show("fatalities_number_expert")
# Show inputs for cumulated impacts option
shinyjs::hide("fatalities_input_type")
shinyjs::show("farm_number_cumulated")
shinyjs::show("fatalities_mat_cumulated")
# Show inputs for multiple scenario
if(input$analysis_choice == "multi_scenario"){
shinyjs::hide("fatalities_input_type")
shinyjs::show("fatalities_vec_scenario")
}
# 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")
}
shinyjs::show("pop_size_number_expert")
# 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")
}
shinyjs::show("pop_growth_number_expert")
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$carrying_cap_input_type == "itvl"){
shinyjs::show("carrying_capacity_lower")
shinyjs::show("carrying_capacity_upper")
}
if(input$carrying_cap_input_type == "val"){
shinyjs::show("carrying_capacity_mean")
shinyjs::show("carrying_capacity_se")
}
if(input$carrying_cap_input_type == "eli_exp"){
shinyjs::show("carrying_cap_number_expert")
shinyjs::show("carrying_cap_mat_expert")
shinyjs::show("carrying_cap_run_expert")
}
}
# Show inputs vital rates part
}) # en observe show/hide
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##############################################
## Reactive values
out <- reactiveValues(run = NULL, msg = NULL, analysis_choice = NULL)
thierrychambert
committed
rv <- reactiveValues(distAVG = NULL, dist05p = NULL)
thierrychambert
committed
ready <- reactiveValues(fatalities = TRUE, pop_size = TRUE, pop_growth = TRUE, carrying_capacity = TRUE)
nsim = NULL,
cumulated_impacts = FALSE,
fatalities_mean_nb = NULL,
fatalities_se = NULL,
fatalities_se_nb = NULL,
onset_time = NULL,
onset_year = NULL,
pop_size_mean = NULL,
pop_size_se = NULL,
pop_growth_mean = NULL,
pop_growth_se = NULL,
fecundities = NULL,
survivals = NULL,
s_calibrated = NULL,
f_calibrated = NULL,
vr_calibrated = NULL,
carrying_capacity_mean = NULL,
carrying_capacity_se = NULL,
theta = NULL,
model_demo = NULL,
coeff_var_environ = NULL,
fatal_constant = NULL,
fatalities_eli_result = NULL,
pop_size_eli_result = NULL,
pop_growth_eli_result = NULL,
carrying_cap_eli_result = NULL
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##############################################
## Define some functions
##-------------------------------------------
# Get lambda from +/-X% growth rate
make_lambda <- function(pop_growth) 1 + (pop_growth/100)
#####
#####
##------------------------------------------
## Update elicitation matrices
##------------------------------------------
###############################
## Cumulated Impacts Matrix
##-----------------------------
observeEvent({
input$farm_number_cumulated
}, {
req(input$farm_number_cumulated > 0)
current_mat <- input$fatalities_mat_cumulated
n_farm <- input$farm_number_cumulated
if(n_farm > nrow(current_mat)){
fill_mat <- c(as.vector(t(current_mat)), rep(NA,(3*(n_farm-nrow(current_mat)))))
}else{
fill_mat <- as.vector(t(current_mat[1:n_farm,]))
}
updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
value = matrix(fill_mat, nrow = n_farm, ncol = 3, byrow = TRUE,
dimnames = list(paste("Parc", c(1:n_farm)),
c("Moyenne",
"Erreur-type",
########################
## Fatalities Matrix
##----------------------
observeEvent({
input$fatalities_number_expert
}, {
req(input$fatalities_number_expert > 0)
current_mat <- input$fatalities_mat_expert
n_experts <- input$fatalities_number_expert
if(n_experts > nrow(current_mat)){
fill_mat <- c(as.vector(t(current_mat)), rep(NA,(5*(n_experts-nrow(current_mat)))))
}else{
fill_mat <- as.vector(t(current_mat[1:n_experts,]))
}
updateMatrixInput(session, inputId = "fatalities_mat_expert",
value = matrix(fill_mat, nrow = n_experts, ncol = 5, byrow = TRUE,
dimnames = list(paste0("#", 1:n_experts),
c("Poids", "Min", "Best", "Max", "% IC" ))
)
)
})
#####
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
########################
## Pop Size Matrix
##----------------------
observeEvent({
input$pop_size_number_expert
}, {
req(input$pop_size_number_expert > 0)
current_mat <- input$pop_size_mat_expert
n_experts <- input$pop_size_number_expert
if(n_experts > nrow(current_mat)){
fill_mat <- c(as.vector(t(current_mat)), rep(NA,(5*(n_experts-nrow(current_mat)))))
}else{
fill_mat <- as.vector(t(current_mat[1:n_experts,]))
}
updateMatrixInput(session, inputId = "pop_size_mat_expert",
value = matrix(fill_mat, nrow = n_experts, ncol = 5, byrow = TRUE,
dimnames = list(paste0("#", 1:n_experts),
c("Poids", "Min", "Best", "Max", "% IC" ))
)
)
})
#####
########################
## Pop Growth Matrix
##----------------------
observeEvent({
input$pop_growth_number_expert
}, {
req(input$pop_growth_number_expert > 0)
current_mat <- input$pop_growth_mat_expert
n_experts <- input$pop_growth_number_expert
if(n_experts > nrow(current_mat)){
fill_mat <- c(as.vector(t(current_mat)), rep(NA,(5*(n_experts-nrow(current_mat)))))
}else{
fill_mat <- as.vector(t(current_mat[1:n_experts,]))
}
updateMatrixInput(session, inputId = "pop_growth_mat_expert",
value = matrix(fill_mat, nrow = n_experts, ncol = 5, byrow = TRUE,
dimnames = list(paste0("#", 1:n_experts),
c("Poids", "Min", "Best", "Max", "% IC" ))
)
)
})
#####
############################
## Carrying Capacity Matrix
##--------------------------
observeEvent({
input$carrying_cap_number_expert
}, {
req(input$carrying_cap_number_expert > 0)
current_mat <- input$carrying_cap_mat_expert
n_experts <- input$carrying_cap_number_expert
if(n_experts > nrow(current_mat)){
fill_mat <- c(as.vector(t(current_mat)), rep(NA,(5*(n_experts-nrow(current_mat)))))
}else{
fill_mat <- as.vector(t(current_mat[1:n_experts,]))
}
updateMatrixInput(session, inputId = "carrying_cap_mat_expert",
value = matrix(fill_mat, nrow = n_experts, ncol = 5, byrow = TRUE,
dimnames = list(paste0("#", 1:n_experts),
c("Poids", "Min", "Best", "Max", "% IC" ))
)
)
})
#####
#####
##--------------------------------------------
##--------------------------------------------
# Function to run the elication analysis
func_eli <- function(mat_expert){
t_mat_expert <- t(mat_expert)
vals <- t_mat_expert[2:4,]
Cp <- t_mat_expert[5,]
weights <- t_mat_expert[1,]
out <- elicitation(vals, Cp, weights)
return(list(out = out, mean = out$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$pop_growth_mat_expert))){
lambda_mat_expert <- input$pop_growth_mat_expert
lambda_mat_expert[,2:4] <- make_lambda(lambda_mat_expert[,2:4])
param$pop_growth_eli_result <- func_eli(lambda_mat_expert)
output$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, 2)), side = 3, line = 4, cex = 1.2, adj = 0)
if(show_mean) mtext(text = paste("Moyenne = ", round(mu, 2)), 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)
thierrychambert
committed
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
} # 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
}
}
thierrychambert
committed
# 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],
thierrychambert
committed
se = input$fatalities_mat_cumulated[,2],
nparc = input$farm_number_cumulated)
thierrychambert
committed
} # 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$distri_plot <- NULL
}
}, ignoreInit = FALSE)
#####
#####
##-------------------------------------------------
## Display parameter values (on the side panel)
##-------------------------------------------------
#################################
##-------------------------------
## UNIT
output$fatalities_unit_info <- renderText({
if(!is.null(input$fatalities_unit)){
if(input$fatalities_unit == "h"){
paste0("Taux de mortalit")
} else {
paste0("Nombre de mortalits")
}
}
})
## Values
output$fatalities_mean_info <- renderText({
if(input$fatalities_unit == "h") add_perc <- "%" else add_perc <- ""
paste0(c("Moyenne : ",
paste0(tail(param$fatalities_mean, -1), add_perc, collapse = ", ")
), collapse = "")
})
output$fatalities_se_info <- renderText({
if(input$fatalities_unit == "h") add_perc <- "%" else add_perc <- ""
paste0(c("Erreur-type : ",
paste0(tail(param$fatalities_se, -1), add_perc, collapse = ", ")
), collapse = "")
})
thierrychambert
committed
#################################
## Poplutation size
##-------------------------------
## UNIT
thierrychambert
committed
output$pop_size_unit_info <- renderText({
if(!is.null(param$pop_size_unit)){
if(param$pop_size_unit == "Npair"){
paste0("Nombre de couple")
thierrychambert
committed
paste0("Effectif total")
thierrychambert
committed
output$pop_size_mean_info <- renderText({ paste0("Moyenne : ", param$pop_size_mean) })
output$pop_size_se_info <- renderText({ paste0("Erreur-type : ", param$pop_size_se) })
## Show Popsize by age (table)
# Function to create the table
make_mat_popsizes <- function(data_sf, species, pop_size, pop_size_unit, survivals, fecundities){
nam <- data_sf %>%
unlist %>%
as.vector
matrix(round(pop_vector(pop_size = pop_size, pop_size_type = pop_size_unit, s = survivals, f = fecundities)),
nrow = 1,
dimnames = list("Effectifs", nam)
)
}
# Display the table (Note the delay : piece is just there to avoid an error message - time for parameters to be "loaded in")
output$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$fecundities)
} # end if
},
width = "500px",
rownames = FALSE,
digits = 0)
)
#################################
## Population growth
##-------------------------------
thierrychambert
committed
output$pop_growth_mean_info <- renderText({ paste0("Moyenne : ", param$pop_growth_mean) })
output$pop_growth_se_info <- renderText({ paste0("Erreur-type : ", param$pop_growth_se) })
#################################
thierrychambert
committed
## Carrying capacity
##-------------------------------
# UNIT (like pop size)
thierrychambert
committed
# Source info "unit"
if(is.null(param$pop_size_unit)){
unit1 <- input$pop_size_unit
}else{
unit1 <- param$pop_size_unit
}
thierrychambert
committed
thierrychambert
committed
if(unit1 == "Npair"){
info1 <- paste0("Nombre de couple")
thierrychambert
committed
info1 <- paste0("Effectif total")
paste0(info1, " : ", param$carrying_capacity_mean)
#################################
##-------------------------------
# Function to create the matrix
make_mat_vr <- function(data_sf, species){
out_mat <- data_sf %>%
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
select(classes_age, survie, fecondite)
return(out_mat)
}
# Update the vital rate matrix (mat_fill_vr) when changing species in the list
observeEvent({
input$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$fecondite
updateMatrixInput(session, inputId = "mat_fill_vr",
value = matrix(data = c(survivals, fecundities),
nrow = number_age_class,
ncol = 2,
dimnames = list(ages, c("Survie", "Fcondit"))))
} # end if 2
} # end if 1
}) # end observeEvent species_list
# Display vital rates output table
# Display intrinsic lambda (based solely on Leslie matrix)
delay(ms = 300,
output$lambda0_info <- renderUI({
lam <- lambda(build_Leslie(s = input$mat_fill_vr[,1], f = input$mat_fill_vr[,2]))
withMathJax(sprintf("$$\\lambda = %.02f$$", lam))
})
)
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
#####
#################################
## Dispersal
##-------------------------------
observeEvent({
input$species_choice
}, {
distAVG <- species_data %>%
filter(NomEspece == input$species_choice) %>%
select(DistDispMoyKM)
rv$distAVG <- round(distAVG, 1)
rv$dist05p <- round(-log(0.05)*rv$distAVG, 1)
})
output$dispersal_mean_info <- renderText({
paste0("Distance moyenne de dispersion : ", rv$distAVG, " km")
})
output$dispersal_d05p_info <- renderText({
paste0("Distance quiv. 5% de dispersion : ", rv$dist05p, " km")
})
#####
##--------------------------------------------
## Select parameter values for simulations
##--------------------------------------------
thierrychambert
committed
# Functions to calculate mean and SD from lower & upper values
get_mu <- function(lower, upper) (lower + upper)/2
get_sd <- function(lower, upper, coverage) ((abs(upper - lower)/2))/qnorm(1-((1-coverage)/2))
#################################
## Cumulated impacts or not ?
##-------------------------------
observeEvent({
input$run
}, {
if(input$analysis_choice == "cumulated"){
param$cumulated_impacts = TRUE
} else {
param$cumulated_impacts = FALSE
} # end if
}) # end observeEvent
#################################
## Fatalities
##-------------------------------
observe({
# Case 1 : Not cumulated effects (if1)
if(input$analysis_choice == "single_farm"){
# 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
thierrychambert
committed
param$fatalities_se <- c(0, round(param$fatalities_eli_result$SE, 3))
ready$fatalities <- TRUE
} else {
ready$fatalities <- FALSE
}
} else {