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Slope_Stan_NONiCAR_route_cmdstan.R
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Slope_Stan_NONiCAR_route_cmdstan.R
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## building a BYM route-level trend model for the BBS
library(bbsBayes)
library(tidyverse)
library(cmdstanr)
# library(rstan)
# rstan_options(auto_write = TRUE, javascript = FALSE)
# library(shinystan)
library(sf)
library(spdep)
# library(doParallel)
# library(foreach)
library(ggforce)
#library(tidybayes)
#source("functions/mungeCARdata4stan.R")
source("functions/neighbours_define.R") ## function to define neighbourhood relationships
source("functions/prepare-jags-data-alt.R") ## small alteration of the bbsBayes function
source("functions/get_basemap_function.R") ## loads one of the bbsBayes strata maps
source("functions/posterior_summary_functions.R") ## functions similar to tidybayes that work on cmdstanr output
## changes captured in a commit on Nov 20, 2020
# load and stratify CASW data ---------------------------------------------
#species = "Pacific Wren"
#species = "Barred Owl"
strat = "bbs_usgs"
model = "slope"
strat_data = stratify(by = strat)
firstYear = 2004
lastYear = 2019
allspecies.eng = strat_data$species_strat$english
species_list = allspecies.eng#[-which(allspecies.eng %in% species_list)]
# optional removing the non-Canadian data ------------------------------------------
Canada_only <- FALSE
if(Canada_only){
scope = "Canada"
names_strata <- get_composite_regions(strata_type = strat) ## bbsBayes function that gets a list of the strata names and their composite regions (provinces, BCRs, etc.)
us_strata_remove <- names_strata[which(names_strata$national == "US"),"region"] # character vector of the strata names to remove from data
}else{
scope = "RangeWide"
us_strata_remove <- NULL
}
# SPECIES LOOP ------------------------------------------------------------
output_dir <- "G:/BBS_iCAR_route_trends/output"
#output_dir_simp <- "G:/BBS_iCAR_route_trends/output"
#species = species_list[748]
selSpecies = c("Boat-tailed Grackle","Ring-billed Gull","Dusky Flycatcher",
"Tennessee Warbler","Cape May Warbler","Northern Bobwhite")
sp_type = data.frame(species = selSpecies,
spatial_pattern = c("weak","weak","weak",
"strong","strong","strong"))
# model run ---------------------------------------------------------------
for(species in selSpecies){
species_f <- gsub(species,pattern = " ",replacement = "_",fixed = T)
sp_file <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_iCAR.RData")
#if(file.exists(sp_file)){
sp_file_new <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_NONiCAR.RData")
# load the output file from the spatial version of the model run ----------
#series of if statements that skip analysing hybrids, composite species groups, etc.
if(grepl(pattern = "hybrid",x = species)){next}
if(grepl(pattern = " x ",x = species)){next}
if(grepl(pattern = "/",fixed = TRUE,x = species)){next}
if(substr(x = species,1,1) == "("){next}
load(sp_file)
# stan_data[["N_edges"]] = NULL
# stan_data[["node1"]] = NULL
# stan_data[["node2"]] = NULL
mod.file = "models/slope_NONiCAR_route2.stan"
## compile model
slope_model <- cmdstan_model(mod.file)
init_def <- function(){ list(noise_raw = rnorm(stan_data$ncounts,0,0.1),
alpha_raw = rnorm(stan_data$nroutes,0,0.1),
ALPHA = 0,
BETA = 0,
eta = 0,
obs_raw = rnorm(stan_data$nobservers,0,0.1),
sdnoise = 0.2,
sdobs = 0.1,
sdbeta_rand = runif(1,0.01,0.1),
beta_raw_rand = rnorm(stan_data$nroutes,0,0.01))}
slope_stanfit <- slope_model$sample(
data=stan_data,
refresh=25,
chains=3, iter_sampling=1000,
iter_warmup=1000,
parallel_chains = 3,
#pars = parms,
adapt_delta = 0.8,
max_treedepth = 14,
seed = 123,
init = init_def)
out_base <- paste0(species_f,"_Non_spatial_",scope,"_",firstYear)
# export to csv and read in as rstan --------------------------------------
slope_stanfit$save_output_files(dir = output_dir,
basename = out_base,
random = FALSE,
timestamp = FALSE)
csv_files <- dir(output_dir,pattern = out_base,full.names = TRUE)
#slope_stanfit$save_object(file = paste0(output_dir,"/",out_base,"_gamye_iCAR.RDS"))
shiny_explore <- FALSE
if(shiny_explore){
sl_rstan <- rstan::read_stan_csv(csv_files)
launch_shinystan(as.shinystan(sl_rstan))
loo_stan = loo(sl_rstan)
}
save(list = c("slope_stanfit",
"out_base",
"stan_data",
"jags_data",
"route_map",
"realized_strata_map",
"firstYear",
"sp_file_new",
"species_f",
"csv_files",
"output_dir"),
file = sp_file_new)
#}
print(species)
}
#stopCluster(cl = cluster)
#
# PLOTTING and trend output -----------------------------------------------
# library(tidybayes)
# route_trajectories <- FALSE #set to FALSE to speed up mapping
#
# maps = vector(mode = "list",length = 400)
# maps2 = vector(mode = "list",length = 400)
#
# maps3 = vector(mode = "list",length = 400)
#
# maps_rand = vector(mode = "list",length = 400)
# maps_space = vector(mode = "list",length = 400)
#
# trends_out_space <- NULL
# trends_out_rand <- NULL
# sdbeta_dif <- NULL
# sdbeta_space_rand <- NULL
jj <- 0
LC = 0.05
UC = 0.95
output_dir <- "G:/BBS_iCAR_route_trends/output"
strata_map <- get_basemap(strata_type = strat,
transform_laea = TRUE,
append_area_weights = FALSE)
loo_stan_spatial <- vector(mode = "list",length = length(selSpecies))
loo_stan_NONspatial <- loo_stan_spatial
trends_out <- NULL
# fix this section to compare spatial and non-spatial ---------------------
library(loo)
for(species in selSpecies){
species_f <- gsub(species,pattern = " ",replacement = "_",fixed = T)
sp_file_new <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_NONiCAR.RData")
sp_file <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_iCAR.RData")
#sp_file <- paste0("output/",species,"Canadian_",firstYear,"_",lastYear,"_slope_route_iCAR2.RData")
if(file.exists(sp_file_new)){
load(sp_file)
sl_rstan <- rstan::read_stan_csv(csv_files)
loo_stan_spatial[[species]] = loo(sl_rstan)
# add trend and abundance ----------------------------------------
beta_samples = posterior_samples(sl_rstan,"beta",
dims = "s")
slopes = beta_samples %>% group_by(s) %>%
summarise(b_spat = mean(.value),
lci_spat = quantile(.value,LC),
uci_spat = quantile(.value,UC),
sd_spat = sd(.value),
prec_spat = 1/var(.value),
trend_spat = mean((exp(.value)-1)*100),
lci_trend_spat = quantile((exp(.value)-1)*100,LC),
uci_trend_spat = quantile((exp(.value)-1)*100,UC),
.groups = "keep")
alpha_samples = posterior_samples(sl_rstan,"alpha",
dims = "s")
interc = alpha_samples %>% group_by(s) %>%
summarise(abund_spat = mean(exp(.value)),
lci_i_spat = quantile(exp(.value),LC),
uci_i_spat = quantile(exp(.value),UC),
sd_i_spat = sd(exp(.value)),
prec_i_spat = 1/var(exp(.value)),
.groups = "keep")
#plot(log(interc$i),slopes$b)
slops_int = inner_join(slopes,interc,by = "s")
slops_int$routeF = slops_int$s
route_map_df = as.data.frame(route_map)
route_map_out = left_join(route_map_df,slops_int,by = "routeF")
route_map_out$species <- species
# non-spatial version -----------------------------------------------------
load(sp_file_new)
sl_rstan <- rstan::read_stan_csv(csv_files)
loo_stan_NONspatial[[species]] = loo(sl_rstan)
# add trend and abundance ----------------------------------------
beta_samples = posterior_samples(sl_rstan,"beta",
dims = "s")
slopes = beta_samples %>% group_by(s) %>%
summarise(b_NONspat = mean(.value),
lci_NONspat = quantile(.value,LC),
uci_NONspat = quantile(.value,UC),
sd_NONspat = sd(.value),
prec_NONspat = 1/var(.value),
trend_NONspat = mean((exp(.value)-1)*100),
lci_trend_NONspat = quantile((exp(.value)-1)*100,LC),
uci_trend_NONspat = quantile((exp(.value)-1)*100,UC),
.groups = "keep")
alpha_samples = posterior_samples(sl_rstan,"alpha",
dims = "s")
interc = alpha_samples %>% group_by(s) %>%
summarise(abund_NONspat = mean(exp(.value)),
lci_i_NONspat = quantile(exp(.value),LC),
uci_i_NONspat = quantile(exp(.value),UC),
sd_i_NONspat = sd(exp(.value)),
prec_i_NONspat = 1/var(exp(.value)),
.groups = "keep")
#plot(log(interc$i),slopes$b)
slops_int = inner_join(slopes,interc,by = "s")
slops_int$routeF = slops_int$s
route_map_out = left_join(route_map_out,slops_int,by = "routeF")
trends_out <- bind_rows(trends_out,route_map_out)
}
}
save(list = c("loo_stan_NONspatial","loo_stan_spatial",
"trends_out"),
file = "output/loo_prediction_comparison_spatial_NON.RData")
load("output/loo_prediction_comparison_spatial_NON.RData")
# full loo ----------------------------------------------------------------
loo_full <- NULL
for(species in selSpecies){
tmp <- data.frame(looic = loo_stan_spatial[[species]]$estimates["looic","Estimate"],
se_looic = loo_stan_spatial[[species]]$estimates["looic","SE"],
model = "Spatial")
tmp2 <- data.frame(looic = loo_stan_NONspatial[[species]]$estimates["looic","Estimate"],
se_looic = loo_stan_NONspatial[[species]]$estimates["looic","SE"],
model = "Non_Spatial")
tmp3 = bind_rows(tmp,tmp2)
tmp3$species = species
loo_full <- bind_rows(loo_full,tmp3)
}
loo_full$lci <- loo_full$looic - (loo_full$se_looic*(1.96*2))
loo_full$uci <- loo_full$looic + (loo_full$se_looic*(1.96*2))
loo_comp_plot <- ggplot(data = loo_full,aes(x = species,y = looic,colour = model,group = model))+
geom_point(position = position_dodge(width = 0.3))+
geom_errorbar(aes(ymin = lci,ymax = uci),alpha = 0.3,width = 0,position = position_dodge(width = 0.3))+
coord_flip()
print(loo_comp_plot)
# pointwise loo -----------------------------------------------------------
loo_point <- NULL
for(species in selSpecies){
species_f <- gsub(species,pattern = " ",replacement = "_",fixed = T)
sp_file <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_iCAR.RData")
load(sp_file)
orig_dat_spatial <- data.frame(count_spat = stan_data$count,
year_spat = stan_data$year,
observer_spat = stan_data$observer,
routeF_spat = stan_data$route)
sp_route_file <- paste0(species_f,"_route_data.RData")
load(paste0("route_maps/",sp_route_file))
route_df <- as.data.frame(route_map) %>%
select(route,routeF,strat)
orig_dat_spatial <- orig_dat_spatial %>%
left_join(.,route_df, by = c("routeF_spat"="routeF"))
# sp_file_new <- paste0(output_dir,"/",species_f,"_",scope,"_",firstYear,"_",lastYear,"_slope_route_NONiCAR.RData")
#
# load(sp_file_new)
#
#
# orig_dat_non <- data.frame(count_non = stan_data$count,
# year_non = stan_data$year,
# observer_non = stan_data$observer,
# routeF_non = stan_data$route)
#
#
# tmp = bind_cols(orig_dat_spatial,orig_dat_non)
#
tmp <- as.data.frame(loo_stan_spatial[[species]]$pointwise)
names(tmp) <- paste(names(tmp),"spatial",sep = "_")
tmp2 <- as.data.frame(loo_stan_NONspatial[[species]]$pointwise)
names(tmp2) <- paste(names(tmp2),"NONspatial",sep = "_")
orig_dat <- orig_dat_spatial %>%
bind_cols(.,tmp) %>%
bind_cols(.,tmp2) %>%
mutate(species = species,
dif_elpd_spat_non = elpd_loo_spatial - elpd_loo_NONspatial)
loo_point <- bind_rows(loo_point,orig_dat)
}
route_n <- loo_point %>%
group_by(species,route,routeF_spat) %>%
summarise(n_surv = n(),
mean_count = mean(count_spat),
mean_dif_elpd = mean(dif_elpd_spat_non))
#obs_mean <-
loo_point <- loo_point %>%
left_join(.,route_n,by = c("species","routeF_spat","route")) %>%
mutate(log_count1 = log(count_spat+1))
bi_plot <- ggplot(data = loo_point,aes(x = elpd_loo_spatial,y = elpd_loo_NONspatial))+
geom_point(aes(colour = n_surv),alpha = 0.2)+
geom_abline(intercept = 0,slope = 1)+
facet_wrap(~species,nrow = 3,ncol = 3,scales = "free")
print(bi_plot)
dif_plot_n <- ggplot(data = loo_point,aes(y = dif_elpd_spat_non,x = n_surv))+
geom_point(alpha = 0.2,position = position_jitter(width = 0.3))+
geom_abline(intercept = 0,slope = 0)+
geom_smooth(method = "lm")+
facet_wrap(~species,nrow = 3,ncol = 3,scales = "free")
print(dif_plot_n)
dif_plot_count <- ggplot(data = loo_point,aes(y = dif_elpd_spat_non,x = log_count1))+
geom_point(alpha = 0.2,position = position_jitter(width = 0.3))+
geom_abline(intercept = 0,slope = 0)+
geom_smooth(method = "lm")+
facet_wrap(~species,nrow = 3,ncol = 3,scales = "free")
print(dif_plot_count)
dif_plot_mean_count <- ggplot(data = route_n,aes(y = mean_dif_elpd,x = mean_count))+
geom_point(alpha = 0.2,position = position_jitter(width = 0.3))+
geom_abline(intercept = 0,slope = 0)+
scale_x_log10()+
geom_smooth(method = "lm")+
facet_wrap(~species,nrow = 3,ncol = 3,scales = "free")
print(dif_plot_mean_count)
# compare trend predictions -----------------------------------------
####
trends_comp <- left_join(trends_out,sp_type,by = "species") %>%
arrange(.,spatial_pattern,species,routeF) %>%
filter(species %in% c("Ring-billed Gull",
"Northern Bobwhite")) %>%
mutate(cv_abund_spat = sd_spat/abund_spat,
cv_abund_NONspat = sd_NONspat/abund_NONspat) %>%
group_by(species) %>%
mutate(rel_prec_trend = scale(sd_NONspat),
rel_prec_abund = scale(sd_i_NONspat))
bi_plot_trends <- ggplot(data = trends_comp,aes(x = trend_spat,y = trend_NONspat))+
geom_point(aes(colour = rel_prec_trend),alpha = 0.5)+
geom_abline(intercept = 0,slope = 1)+
xlab("Spatial Trend Estimate (%/year)")+
ylab("Non Spatial Trend Estimate (%/year)")+
scale_colour_viridis_c(begin = 0, end = 0.9,direction = 1,
guide = guide_legend(title = "relative SD of trend",
reverse = TRUE))+
theme_minimal()+
#theme(legend.position = "none")+
facet_wrap(~species,nrow = 2,ncol = 2,scales = "free")
pdf(file = paste0("Figures/bi_plot_trends_spatial_non_by_pred.pdf"),
width = 8,
height = 4)
print(bi_plot_trends)
dev.off()
bi_plot_prec <- ggplot(data = trends_comp,aes(x = prec_spat,y = prec_NONspat))+
geom_point(alpha = 0.3)+
#geom_point(aes(colour = strat),alpha = 0.5)+
geom_abline(intercept = 0,slope = 1)+
xlab("Spatial Precision Trend Estimate (%/year)")+
ylab("Non Spatial Precision Trend Estimate (%/year)")+
# scale_colour_viridis_d(begin = 0, end = 0.9,direction = 1,
# guide = guide_legend(title = "relative SD of trend",
# reverse = TRUE))+
theme_minimal()+
theme(legend.position = "none")+
facet_wrap(~species,nrow = 2,ncol = 2,scales = "free")
print(bi_plot_prec)
# comparing abundance predictions -----------------------------------------
bi_plot_abund <- ggplot(data = trends_comp,aes(x = abund_spat,y = abund_NONspat))+
geom_point(aes(colour = rel_prec_abund),alpha = 0.5)+
geom_abline(intercept = 0,slope = 1)+
xlab("Spatial Abundance Estimate (%/year)")+
ylab("Non Spatial Abundance Estimate (%/year)")+
scale_colour_viridis_c(begin = 0, end = 0.9,direction = 1,
guide = guide_legend(title = "relative SD of abundance",
reverse = TRUE))+
theme_minimal()+
facet_wrap(~species,nrow = 2,ncol = 2,scales = "free")
print(bi_plot_abund)
bi_plot_prec_abund <- ggplot(data = trends_comp,aes(x = prec_i_spat,y = prec_i_NONspat))+
geom_point(alpha = 0.3)+
#geom_point(aes(colour = strat),alpha = 0.5)+
geom_abline(intercept = 0,slope = 1)+
xlab("Spatial Precision Trend Estimate (%/year)")+
ylab("Non Spatial Precision Trend Estimate (%/year)")+
# scale_colour_viridis_d(begin = 0, end = 0.9,direction = 1,
# guide = guide_legend(title = "relative SD of trend",
# reverse = TRUE))+
theme_minimal()+
facet_wrap(~species,nrow = 2,ncol = 2,scales = "free")
print(bi_plot_prec_abund)
bi_plot_prec_abund <- ggplot(data = trends_comp,aes(x = cv_abund_spat,y = cv_abund_NONspat))+
geom_point(alpha = 0.3)+
#geom_point(aes(colour = strat),alpha = 0.5)+
geom_abline(intercept = 0,slope = 1)+
xlab("Spatial Precision Trend Estimate (%/year)")+
ylab("Non Spatial Precision Trend Estimate (%/year)")+
# scale_colour_viridis_d(begin = 0, end = 0.9,direction = 1,
# guide = guide_legend(title = "relative SD of trend",
# reverse = TRUE))+
theme_minimal()+
facet_wrap(~species,nrow = 2,ncol = 2,scales = "free")
print(bi_plot_prec_abund)
jj <- jj+1
# laea = st_crs("+proj=laea +lat_0=40 +lon_0=-95") # Lambert equal area coord reference system
#
# locat = system.file("maps",
# package = "bbsBayes")
# map.file = "BBS_USGS_strata"
#
# strata_map = read_sf(dsn = locat,
# layer = map.file)
# strata_map = st_transform(strata_map,crs = laea)
#
# realized_strata_map = filter(strata_map,ST_12 %in% unique(jags_data$strat_name))
#
# strata_list <- data.frame(ST_12 = unique(jags_data$strat_name),
# strat = unique(jags_data$strat))
#
#
# realized_strata_map <- inner_join(realized_strata_map,strata_list, by = "ST_12")
#
####
# random effect plus mean component of slope ----------------------------------------
# BETA_samples = posterior_samples(sl_rstan,BETA) %>%
# rename(BETA = .value) %>%
# ungroup() %>%
# select(BETA,.draw)
#
# beta_rand_samples = posterior_samples(sl_rstan,beta_rand[s]) %>%
# rename(beta_rand = .value) %>%
# ungroup() %>%
# select(beta_rand,.draw,s)
#
# beta_rand_samples <- inner_join(beta_rand_samples,BETA_samples,by = c(".draw"))
#
# slopes_rand_full = beta_rand_samples %>% group_by(s) %>%
# summarise(b = mean(beta_rand + BETA),
# lci = quantile(beta_rand + BETA,LC),
# uci = quantile(beta_rand + BETA,UC),
# sd = sd(beta_rand + BETA),
# prec = 1/var(beta_rand + BETA),
# .groups = "keep")
#
# slopes_rand_full_int = inner_join(slopes_rand_full,interc,by = "s")
# slopes_rand_full_int$routeF = slopes_rand_full_int$s
beta_rand_samples = posterior_samples(sl_rstan,
"beta_rand",
dims = "s")
slopes_rand = beta_rand_samples %>% group_by(s) %>%
summarise(b = mean(.value),
lci = quantile(.value,LC),
uci = quantile(.value,UC),
sd = sd(.value),
prec = 1/var(.value),
trend = mean((exp(.value)-1)*100),
lci_trend = quantile((exp(.value)-1)*100,LC),
uci_trend = quantile((exp(.value)-1)*100,UC),
.groups = "keep")
slops_rand_int = inner_join(slopes_rand,interc,by = "s")
slops_rand_int$routeF = slops_rand_int$s
# spatial component of slope ----------------------------------------
beta_space_samples = posterior_samples(sl_rstan,"beta_space",
dims = "s")
slopes_space = beta_space_samples %>% group_by(s) %>%
summarise(b = mean(.value),
lci = quantile(.value,LC),
uci = quantile(.value,UC),
sd = sd(.value),
prec = 1/var(.value),
trend = mean((exp(.value)-1)*100),
lci_trend = quantile((exp(.value)-1)*100,LC),
uci_trend = quantile((exp(.value)-1)*100,UC),
.groups = "keep")
slops_space_int = inner_join(slopes_space,interc,by = "s")
slops_space_int$routeF = slops_space_int$s
# Compare spatial and random variation ------------------------------------
sdbeta_rand_tmp_samples <- posterior_samples(sl_rstan,
"sdbeta_rand")
sdbeta_space_tmp_samples <- posterior_samples(sl_rstan,
"sdbeta_space")
sdbeta_space_rand_tmp_samples <- bind_rows(sdbeta_rand_tmp_samples,
sdbeta_space_tmp_samples)
sdbeta_space_rand_tmp <- sdbeta_space_rand_tmp_samples %>%
group_by(.variable) %>%
summarise(mean = mean((.value)),
lci = quantile((.value),LC),
uci = quantile((.value),UC),
sd = sd((.value)),
.groups = "keep") %>%
mutate(species = species)
#combines all species estimates
sdbeta_space_rand <- bind_rows(sdbeta_space_rand,sdbeta_space_rand_tmp)
# difference rand-spatial -------------------------------------------------
sdbeta_space_tmp_samples <- sdbeta_space_tmp_samples %>%
rename(sd_space = .value) %>%
ungroup() %>%
select(-.variable)
sdbeta_rand_tmp_samples <- sdbeta_rand_tmp_samples %>%
rename(sd_rand = .value)%>%
ungroup() %>%
select(-.variable)
sdbeta_tmp_samples <- inner_join(sdbeta_rand_tmp_samples,sdbeta_space_tmp_samples)
sdbeta_tmp_dif <- sdbeta_tmp_samples %>%
group_by(.draw) %>%
summarise(dif = sd_rand-sd_space) %>%
ungroup() %>%
summarise(mean = mean((dif)),
lci = quantile((dif),LC),
uci = quantile((dif),UC),
sd = sd((dif))) %>%
mutate(species = species)
sdbeta_dif <- bind_rows(sdbeta_dif,sdbeta_tmp_dif)
# Route-level trajectories ------------------------------------------------
if(route_trajectories){
sdnoise_samples = posterior_samples(sl_rstan,"sdnoise")%>%
ungroup() %>%
select(.draw,.value) %>%
rename(sdnoise = .value)
sdobs_samples = posterior_samples(sl_rstan,"sdobs")%>%
ungroup() %>%
select(.draw,.value) %>%
rename(sdobs = .value)
beta_samples <- beta_samples %>%
ungroup() %>%
select(s,.draw,.value) %>%
rename(beta = .value)
alpha_samples <- alpha_samples %>%
ungroup() %>%
select(s,.draw,.value) %>%
rename(alpha = .value)
ab_samples <- inner_join(beta_samples,alpha_samples)
ab_samples <- ab_samples %>%
left_join(.,sdnoise_samples,by = ".draw") %>%
left_join(.,sdobs_samples,by = ".draw")
nyears = stan_data$nyears
fixedyear = stan_data$fixedyear
YEARS = c(min(jags_data$r_year):max(jags_data$r_year))
if(length(YEARS) != nyears){stop("years don't match YEARS =",length(YEARS),"nyears =",nyears)}
ind_fxn = function(a,b,sdn,sdob,y,fy){
i = exp(a + b*(y-fy) + (0.5*(sdn^2))+ (0.5*(sdob^2)))
return(i)
}
### this could be simplified to just estimate the start and end-years
i_samples = NULL
for(yr in 1:nyears){
i_t = ab_samples %>%
mutate(i = ind_fxn(alpha,beta,sdnoise,sdobs,yr,fixedyear),
y = yr,
year = YEARS[yr])
i_samples <- bind_rows(i_samples,i_t)
}
### this could be tweaked to sum across all routes in the original strata
### just join to the strata-route dataframe - route_map
### then add the non-zero-weights for the strata
### then add the area-weights for the strata
### and change the group-by value
indices = i_samples %>% group_by(s,y,year) %>%
summarise(index = mean(i),
lci_i = quantile(i,LC),
uci_i = quantile(i,UC),
sd_i = sd(i),
.groups = "keep")
raw = data.frame(s = stan_data$route,
y = stan_data$year,
count = stan_data$count,
obs = stan_data$observer)
indices = left_join(indices,raw,by = c("y","s"))
rts = route_map %>% tibble() %>%
select(route,routeF,strat) %>%
mutate(s = routeF)
indices = left_join(indices,rts,by = "s")
indices$obs <- factor(indices$obs)
nroutes = stan_data$nroutes
# setting up the plot dimensions
npg = ceiling(nroutes/9)
ncl = 3
nrw = 3
if(npg*9-nroutes < 3){
nrw = 2
npg = ceiling(nroutes/6)
if(npg*6-nroutes < 3){
ncl = 2
npg = ceiling(nroutes/4)
}
}
####
pdf(paste0("trajectories/",species,"_route_trajectories2.pdf"),
width = 11,
height = 8.5)
for(j in 1:npg){
traj = ggplot(data = indices,aes(x = year,y = index,colour = strat))+
geom_ribbon(aes(ymin = lci_i,ymax = uci_i),alpha = 0.4,fill = grey(0.5))+
geom_line()+
geom_point(aes(x = year,y = count, colour = obs), fill = grey(0.5),alpha = 0.5,inherit.aes = FALSE)+
facet_wrap_paginate(~ strat+route,scales = "free",ncol = ncl,nrow = nrw,page = j)+
theme(legend.position = "none")
try(print(traj),silent = TRUE)
}
dev.off()
}
# connect trends to original route names ----------------------------------
route_map_out_rand = left_join(route_map,slops_rand_int,by = "routeF")
route_map_out_rand$species <- species
trends_out_rand <- bind_rows(trends_out_rand,route_map_out_rand)
# slopes_rand_full_int
# route_map_out_rand = left_join(route_map,slopes_rand_full_int,by = "routeF")
# route_map_out_rand$species <- species
#
# trends_out_rand <- bind_rows(trends_out_rand,route_map_out_rand)
route_map_out_space = left_join(route_map,slops_space_int,by = "routeF")
route_map_out_space$species <- species
trends_out_space <- bind_rows(trends_out_space,route_map_out_space)
### setting up boundaries for plots
# load(paste0("route_maps/",species_f,"_route_data.RData"))
strata_bounds <- st_union(realized_strata_map) #union to provide a simple border of the realised strata
bb = st_bbox(strata_bounds)
xlms = as.numeric(c(bb$xmin,bb$xmax))
ylms = as.numeric(c(bb$ymin,bb$ymax))
# add mapping of trends ---------------------------------------------------
plot_trend <- TRUE #set to false to plot the slopes
if(plot_trend){
breaks <- c(-7, -4, -2, -1, -0.5, 0.5, 1, 2, 4, 7)
lgnd_head <- "Trend\n"
trend_title <- "trends"
labls = c(paste0("< ",breaks[1]),paste0(breaks[-c(length(breaks))],":", breaks[-c(1)]),paste0("> ",breaks[length(breaks)]))
labls = paste0(labls, " %/year")
route_map_out$Tplot <- cut(route_map_out$trend,breaks = c(-Inf, breaks, Inf),labels = labls)
route_map_out <- route_map_out %>%
mutate(h_ci = (uci_trend-lci_trend)/2)
route_map_out_space$Tplot <- cut(route_map_out_space$trend,breaks = c(-Inf, breaks, Inf),labels = labls)
route_map_out_rand$Tplot <- cut(route_map_out_rand$trend,breaks = c(-Inf, breaks, Inf),labels = labls)
}else{
breaks <- c(-0.07, -0.04, -0.02, -0.01, -0.005, 0.005, 0.01, 0.02, 0.04, 0.07)
lgnd_head <- "slope\n"
trend_title <- "trend-slopes"
labls = c(paste0("< ",breaks[1]),paste0(breaks[-c(length(breaks))],":", breaks[-c(1)]),paste0("> ",breaks[length(breaks)]))
labls = paste0(labls, " slope")
route_map_out$Tplot <- cut(route_map_out$b,breaks = c(-Inf, breaks, Inf),labels = labls)
route_map_out <- route_map_out %>%
mutate(h_ci = (uci-lci)/2)
route_map_out_space$Tplot <- cut(route_map_out_space$b,breaks = c(-Inf, breaks, Inf),labels = labls)
route_map_out_rand$Tplot <- cut(route_map_out_rand$b,breaks = c(-Inf, breaks, Inf),labels = labls)
}
map_palette <- c("#a50026", "#d73027", "#f46d43", "#fdae61", "#fee090", "#ffffbf",
"#e0f3f8", "#abd9e9", "#74add1", "#4575b4", "#313695")
names(map_palette) <- labls
tmap = ggplot(route_map_out)+
#geom_sf(data = realized_strata_map,colour = gray(0.8),fill = NA)+
geom_sf(data = strata_map,colour = gray(0.8),fill = NA)+
geom_sf(aes(colour = Tplot,size = abund))+
scale_size_continuous(range = c(0.5,3),
name = "Mean abundance")+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
labs(title = paste("DRAFT ",species,trend_title,"by BBS route"),
subtitle = "Route-level trends from a spatial iCAR model, using Stan")
maps[[jj]] <- tmap
png(filename = paste0("Figures/images/",species_f,"_Trends_",firstYear,".png"),
res = 600,
width = 20,
height = 15,
units = "cm")
print(tmap)
dev.off()
tmap2 = ggplot(route_map_out)+
geom_sf(data = strata_map,colour = gray(0.8),fill = NA)+
geom_sf(aes(colour = Tplot,size = abund))+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
theme(legend.position = "none")+
labs(title = paste(species))
maps2[[jj]] <- tmap2
tmap3 = ggplot(route_map_out)+
geom_sf(data = strata_map,colour = gray(0.8),fill = NA)+
geom_sf(aes(colour = Tplot,size = 1/h_ci))+
scale_size_continuous(range = c(0.5,3))+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
labs(title = paste(species))
maps3[[jj]] <- tmap3
tmap_space = ggplot(route_map_out_space)+
geom_sf(data = strata_map,colour = gray(0.8),fill = NA)+
geom_sf(aes(colour = Tplot,size = abund))+
scale_colour_manual(values = map_palette, aesthetics = c("colour"),
guide = guide_legend(reverse=TRUE),
name = paste0(lgnd_head,firstYear,"-",lastYear))+
coord_sf(xlim = xlms,ylim = ylms)+
theme(legend.position = "none")+
labs(title = paste("spatial component"))
maps_space[[jj]] <- tmap_space