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SFA_VAST_fn.R
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SFA_VAST_fn.R
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library(VAST)
library(TMB)
library(tidyverse)
theme_set(theme_bw())
#dyn.load(system.file("executables", package = "VAST") %>% file.path("VAST_v13_1_0.dll"))
### TODO: Replace extrapolation grid with ecoregion shapefile
#ocean <- rnaturalearth::ne_download(scale = 110, type = "ocean", category = "physical")
fit_VAST_model <- function(DataFrame,
Aniso = FALSE,
category_names = c("BET", "SWO"),
SD = FALSE,
X1_formula = ~ 0,
X2_formula = ~ 0,
covariate_data = NULL,
Q1_formula = ~ 0,
Q2_formula = ~ 0,
catchability_data = NULL,
Q1config_k = NULL,
Q2config_k = NULL,
n_factors = 2,
run_model = TRUE,
n_x = 150,
ObsModel_ez = c(10, 2),
CheckForErrors = TRUE,
Omega = c(0, 0),
Epsilon = c(0, 0),
Beta = c("IID", 0),
RhoConfig = c("Beta1" = 3, "Beta2" = 3, "Epsilon1" = 0, "Epsilon2" = 0)) {
settings <- FishStatsUtils::make_settings(n_x = n_x,
Region = "user",
purpose = "ordination",
fine_scale = FALSE,
n_categories = n_factors,
max_cells = n_x)
input_grid <- DataFrame %>%
group_by(Latitude, Longitude) %>%
summarise(Area_km2 = 1) %>%
rename(Lat = Latitude, Lon = Longitude)
#xy_ind <- sf::st_intersects(input_grid %>% sf::st_as_sf(coords = c("Lon", "Lat"), crs = 4326),
# ocean %>% as("sf")) %>%
# sapply(function(x) length(x) > 0)
extrapolation_list <- make_extrapolation_info(Region = "user",
max_cells = n_x,
#input_grid = input_grid[xy_ind, ] %>% as.matrix(),
input_grid = input_grid %>% as.matrix(),
Save_results = FALSE,
nstart = 1)
spatial_list <- make_spatial_info(n_x = n_x,
Lon_i = DataFrame$Longitude,
Lat_i = DataFrame$Latitude,
Extrapolation_List = extrapolation_list,
Method = "Mesh",
#Save_Results = TRUE,
fine_scale = TRUE)
settings$FieldConfig["Omega", ] <- Omega
settings$FieldConfig["Epsilon", ] <- Epsilon
settings$FieldConfig["Beta", ] <- Beta
#### Year effect:
# Default RhoConfig = c("Beta1" = 3, "Beta2" = 3, "Epsilon1" = 0, "Epsilon2" = 0)
# 0 = each year as fixed effect (FE)
# 1 = each year is IID as random effect (RE)
# 2 = Random walk RE
# 3 = single intercept FE
# 4 = AR1 FE
# 5 = AR1 FE by category
settings$RhoConfig <- RhoConfig
data_list <- make_data(b_i = as_units(DataFrame$value, "count"), # Response
a_i = as_units(DataFrame$Eff1, unitless), # Offset
t_i = DataFrame$YearC, # Time
c_iz = DataFrame$variable %>% as.integer() %>% `-`(1), # Category (species)
e_i = model.matrix(~ 0 + variable : FlagCode, DataFrame) %>% # SD per species x country combination
apply(1, function(x) which(x == 1)) %>% as.numeric() %>% `-`(1),
FieldConfig = settings$FieldConfig,
OverdispersionConfig = settings$OverdispersionConfig,
RhoConfig = settings$RhoConfig,
VamConfig = settings$VamConfig,
ObsModel_ez = ObsModel_ez,
covariate_data = covariate_data,
X1_formula = X1_formula,
X2_formula = X2_formula,
catchability_data = catchability_data,
Q1_formula = Q1_formula,
Q2_formula = Q2_formula,
Q1config_k = Q1config_k,
Q2config_k = Q2config_k,
spatial_list = spatial_list,
Options = settings$Options,
Aniso = Aniso,
CheckForErrors = CheckForErrors)
tmb_list <- make_model(TmbData = data_list,
Method = "Mesh",
Version = settings$Version,
RhoConfig = settings$RhoConfig,
loc_x = spatial_list$loc_x
)
tmb_list$Obj$env$beSilent()
if (run_model) {
message("Fitting model...")
parameter_estimates1 <- TMBhelper::fit_tmb(obj = tmb_list$Obj,
#lower = tmb_list$Lower,
#upper = tmb_list$Upper,
control = list(eval.max = 10000, iter.max = 10000),
loopnum = 0,
quiet = TRUE,
getsd = FALSE)
message("Model finished.")
if(SD) {
message("Calculating covariance matrix...")
parameter_estimates1[["SD"]] <- TMB::sdreport(tmb_list$Obj)
message("TMB::sdreport finished.")
}
ParHat = tmb_list$Obj$env$parList(parameter_estimates1$par)
} else {
parameter_estimates1 <- list()
ParHat <- tmb_list$Obj$env$parList()
}
Report <- tmb_list$Obj$report(tmb_list$Obj$env$last.par.best)
Report = amend_output(Report = Report,
TmbData = data_list,
Map = tmb_list$Map,
Sdreport = NULL,
year_labels = DataFrame$YearC %>% unique(),
category_names = category_names,
extrapolation_list = extrapolation_list)
Return = list(data_frame = data.frame(Lat_i = DataFrame$Latitude,
Lon_i = DataFrame$Longitude,
a_i = as_units(DataFrame$Eff1, unitless),
v_i = rep(0, nrow(DataFrame)),
b_i = as_units(DataFrame$value, "count"),
t_i = DataFrame$YearC,
c_iz = DataFrame$variable %>% as.integer() %>% `-`(1)),
extrapolation_list = extrapolation_list,
spatial_list = spatial_list,
data_list = data_list,
tmb_list = tmb_list,
parameter_estimates = parameter_estimates1,
Report = Report,
ParHat = ParHat,
year_labels = DataFrame$YearC %>% unique(),
years_to_plot = 1:length(unique(DataFrame$YearC)),
category_names = category_names,
settings = settings) %>%
structure(class = "fit_model")
##input_args = input_args,
#X1config_cp = X1config_cp,
#X2config_cp = X2config_cp,
#covariate_data = covariate_data,
#X1_formula = X1_formula,
#X2_formula = X2_formula,
#Q1config_k = Q1config_k,
#Q2config_k = Q1config_k,
#catchability_data = catchability_data,
#Q1_formula = Q1_formula,
#Q2_formula = Q2_formula)
return(Return)
}
plot_VAST_results <- function (fit, settings = fit$settings, plot_set = 3, working_dir = paste0(getwd(),
"/"), year_labels = fit$year_labels, years_to_plot = fit$years_to_plot,
category_names = fit$category_names, strata_names = fit$strata_names,
use_biascorr = TRUE, map_list, check_residuals = TRUE, projargs = "+proj=longlat",
zrange, n_samples = 100, calculate_relative_to_average = FALSE,
type = 1, n_cells = NULL, n_cells_residuals = NULL, RotationMethod = "PCA",
quantiles = c(0.05, 0.5, 0.95), ...)
{
if (is.null(fit$Report))
stop("`fit$Report` is missing, please check inputs")
if (is.null(category_names))
category_names = paste0("Category_", 1:fit$data_list$n_c)
dir.create(working_dir, showWarnings = FALSE, recursive = TRUE)
message("\n### Creating plots in directory ", working_dir)
message("\n### Making plots of data availability and knots")
plot_data_args = list(...)
plot_data_args = combine_lists(input = plot_data_args, args_to_use = formalArgs(plot_data),
default = list(Extrapolation_List = fit$extrapolation_list,
Spatial_List = fit$spatial_list, Lat_i = fit$data_frame[,
"Lat_i"], Lon_i = fit$data_frame[, "Lon_i"],
Year_i = fit$data_frame[, "t_i"], PlotDir = working_dir,
year_labels = year_labels, projargs = projargs))
do.call(what = plot_data, args = plot_data_args)
if (missing(map_list)) {
message("\n### Obtaining default settings for plotting maps")
map_list = make_map_info(Region = settings$Region, spatial_list = fit$spatial_list,
Extrapolation_List = fit$extrapolation_list)
}
message("\n### Making plot of anisotropy")
plot_anisotropy(FileName = paste0(working_dir, "Aniso.png"),
Obj = fit$tmb_list$Obj)
plot_biomass_index_args = list(...)
#if (!is.null(fit$parameter_estimates$SD)) {
# message("\n### Making plot of abundance index")
# plot_biomass_index_args = combine_lists(input = plot_biomass_index_args,
# args_to_use = formalArgs(plot_biomass_index), default = list(DirName = working_dir,
# fit = fit, year_labels = year_labels, years_to_plot = years_to_plot,
# use_biascorr = use_biascorr, category_names = category_names,
# strata_names = strata_names))
# Index = do.call(what = plot_biomass_index, args = plot_biomass_index_args)
#}
#else {
# Index = "Not run"
# message("\n### Skipping plot of abundance index; must re-run with standard errors to plot")
#}
#if (!is.null(fit$parameter_estimates$SD) & fit$data_list$n_c >
# 1) {
# message("\n### Making plot of composition data")
# Proportions = calculate_proportion(TmbData = fit$data_list,
# Index = Index, year_labels = year_labels, years_to_plot = years_to_plot,
# use_biascorr = use_biascorr, category_names = category_names,
# DirName = working_dir)
#}
#else {
# Proportions = "Not run"
# message("\n### Skipping plot of composition data; must re-run with standard errors and multiple categories to plot")
#}
#if (!is.null(fit$parameter_estimates$SD)) {
# message("\n### Making plot of spatial indices")
# Range = plot_range_index(Report = fit$Report, TmbData = fit$data_list,
# Sdreport = fit$parameter_estimates$SD, Znames = colnames(fit$data_list$Z_xm),
# PlotDir = working_dir, year_labels = year_labels,
# years_to_plot = years_to_plot, use_biascorr = use_biascorr,
# category_names = category_names)
#}
#else {
# Range = "Not run"
# message("\n### Skipping plot of spatial indices; must re-run with standard errors to plot")
#}
#if ("jointPrecision" %in% names(fit$parameter_estimates$SD) &
# n_samples > 0 & fit$data_list$Options_list$Options["Calculate_Range"] ==
# TRUE) {
# message("\n### Making plot of range edges")
# Edge = plot_range_edge(Obj = fit$tmb_list$Obj, Sdreport = fit$parameter_estimates$SD,
# working_dir = working_dir, year_labels = year_labels,
# years_to_plot = years_to_plot, category_names = category_names,
# n_samples = n_samples, quantiles = quantiles, calculate_relative_to_average = calculate_relative_to_average)
#}
#else {
# Edge = "Not run"
# message("\n### Skipping plot of range edge; only possible if `getJointPrecision=TRUE`, `Options['Calculate_Range']=TRUE`, and `n_samples`>0")
#}
message("\n### Making plots of spatial predictions")
plot_maps_args = list(...)
plot_maps_args = combine_lists(input = plot_maps_args, default = list(fit = fit,
plot_set = plot_set, category_names = category_names,
PlotDF = map_list[["PlotDF"]], MapSizeRatio = map_list[["MapSizeRatio"]],
working_dir = working_dir, year_labels = year_labels,
years_to_plot = years_to_plot, legend_x = map_list[["Legend"]]$x/100,
legend_y = map_list[["Legend"]]$y/100, projargs = projargs,
n_cells = n_cells))
Dens_xt = do.call(what = plot_maps, args = plot_maps_args)
message("\n### Making plots for factors (if present)")
plot_factors_args = list(...)
plot_factors_args = combine_lists(input = plot_factors_args,
default = list(fit = fit, mapdetails_list = map_list,
projargs = projargs, n_cells = n_cells, RotationMethod = RotationMethod,
plotdir = working_dir, category_names = category_names))
Factors = do.call(what = plot_factors, args = plot_factors_args)
if (check_residuals == TRUE) {
message("\n### Making quantile residuals using conditional simulation and package DHARMa")
dharmaRes = summary(fit, what = "residuals", working_dir = working_dir,
type = type, ...)
message("\n### Plotting quantile residuals ")
plot_quantile_residuals(dharmaRes = dharmaRes, fit = fit,
working_dir = working_dir, year_labels = year_labels,
years_to_plot = years_to_plot, n_cells_residuals = n_cells_residuals,
projargs = projargs, ...)
}
else {
message("\n### Skipping quantile residuals using conditional simulation and package DHARMa")
message("\n### Skipping plot of quantile residuals ")
dharmaRes = NULL
}
Return = list(dharmaRes = dharmaRes, #Index = Index, Proportions = Proportions,
#Range = Range,
Dens_xt = Dens_xt, #Edge = Edge,
map_list = map_list,
plot_maps_args = plot_maps_args,
#plot_biomass_index_args = plot_biomass_index_args,
Factors = Factors)
return(invisible(Return))
}
plot_VAST_gg <- function (fit, country_list,
country_index,
do_plot = c("data", "factor", "density"),
plot_set = 3, working_dir = paste0(getwd(), "/"),
year_labels = fit$year_labels, years_to_plot = fit$years_to_plot,
category_names = fit$category_names,
strata_names,
dharma_resid = NA,
ecoregion = FALSE,
...) {
#use_biascorr = TRUE, map_list, check_residuals = TRUE, projargs = "+proj=longlat",
#zrange, n_samples = 100, calculate_relative_to_average = FALSE,
#type = 1, n_cells = NULL, n_cells_residuals = NULL, RotationMethod = "PCA",
#quantiles = c(0.05, 0.5, 0.95), ...) {
if (ecoregion) {
ecoregion_shp <- local({
dir <- "G:/Shared drives/BM shared/1. Projects/EcoTest/ICCAT_shp"
sf::st_read(file.path(dir, "ICCAT_draft_ecoregions_May 2022.shp"))
})
geom_eco <- geom_sf(data = ecoregion_shp %>% as("sf"), colour = "black", fill = NA, inherit.aes = FALSE)
} else {
coast <- rnaturalearth::ne_coastline(scale = 110, returnclass = "sf")
geom_eco <- geom_sf(data = coast, inherit.aes = FALSE)
}
sf::sf_use_s2(FALSE)
# plot data
if ("data" %in% do_plot) {
dat <- fit$data_frame %>%
mutate(b_i = as.numeric(b_i), a_i = as.numeric(a_i),
country = sapply(country_index + 1, function(x) country_list[[x]]),
sp = category_names[c_iz + 1],
D_i = fit$Report$D_i,
strata = strata_names,
Dobs_i = b_i/a_i,
log_Dobs_i = log(Dobs_i),
dharma_resid = dharma_resid)
dat_cc <- dat %>%
filter(b_i > 0) %>%
reshape2::dcast(strata + country ~ sp, value.var = "log_Dobs_i")
g <- GGally::ggpairs(dat_cc,
columns = 3:ncol(dat_cc),
xlab = "log CPUE", ylab = "log CPUE",
aes(colour = country, alpha = 0.2))
ggsave(file.path(working_dir, "obs_pairs.png"), g, height = 6, width = 6)
# Sample correlations
#D_i_corr <- reshape2::acast(dat, list("strata", "sp"), value.var = "D_i")
#samp_corr <- lapply(category_names, function(x) {
# corrdat %>%
# filter(sp == x) %>%
# pull(Dobs_i)
#
#})
#
#cov_j <- fit$Report[[covvec[i]]]
#for(ii in 1:(nrow(cov_j)-1)) {
# for(j in (ii+1):nrow(cov_j)) cov_j[ii, j] <- NA
#}
#
#corr_j <- cov_j %>%
# cov2cor() %>%
# structure(dimnames = list(Var1 = category_names, Var2 = category_names)) %>%
# signif(2) %>%
# reshape2::melt() %>%
# filter(!is.na(value)) %>%
# mutate(Var1 = factor(Var1, levels = category_names %>% rev()),
# Var2 = factor(Var2, levels = category_names))
#
#g <- corr_j %>%
# ggplot(aes(Var2, Var1)) +
# geom_tile(height = 1, width = 1, colour = "black", aes(fill = value), alpha = 0.5) +
# geom_text(aes(label = value)) +
# coord_cartesian(expand = FALSE) +
# scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red")) +
# gfplot::theme_pbs() +
# theme(legend.position = "none") +
# labs(x = "", y = "") +
# guides(fill = "none")
#ggsave(file.path(working_dir, paste0(lab[i], "_corr.png")), g, height = 3, width = 4)
for(cc in unique(dat$c_iz)) { # Category (species)
dat_cc <- dat %>% filter(c_iz == cc)
g <- dat_cc %>%
ggplot(aes(b_i, D_i)) +
geom_abline(slope = 1, intercept = 0) +
geom_point(alpha = 0.2) +
facet_wrap(vars(country), scales = "free") +
labs(x = "Observed", y = "Predicted") +
ggtitle(category_names[cc + 1])
ggsave(file.path(working_dir, paste0("obs_pred_", category_names[cc+1], ".png")), g, height = 6, width = 6)
g <- dat_cc %>%
ggplot(aes(b_i, log(b_i/D_i))) +
geom_hline(yintercept = 0, linetype = 2) +
geom_point(alpha = 0.2) +
facet_wrap(vars(country), scales = "free") +
labs(x = "Observed", y = "log Residual") +
ggtitle(category_names[cc + 1])
ggsave(file.path(working_dir, paste0("residual_", category_names[cc+1], ".png")), g, height = 6, width = 6)
for(country in unique(country_index)) { # Country
dat_out <- dat[country_index == country, ] %>%
filter(c_iz == cc)
# Data
g <- dat_out %>%
ggplot(aes(Lon_i, Lat_i)) +
geom_eco +
coord_sf(xlim = range(dat$Lon_i), ylim = range(dat$Lat_i)) +
geom_jitter(aes(shape = b_i == 0, fill = log(b_i/a_i))) +
facet_wrap(vars(t_i)) +
#facet_grid(vars(t_i), vars(season)) +
labs(x = "Longitude", y = "Latitude", fill = "log CPUE") +
theme_bw() +
theme(panel.spacing = unit(0, "in")) +
scale_fill_viridis_c() +
scale_shape_manual(values = c(21, 4)) +
guides(shape = "none") +
ggtitle(category_names[cc + 1] %>% paste(country_list[[country + 1]]))
ggsave(file.path(working_dir, paste0("data_map_", category_names[cc + 1], "_", country_list[[country + 1]], "_ecoregion.png")),
g, height = 6, width = 8)
# Residual
g <- dat_out %>%
ggplot(aes(Lon_i, Lat_i)) +
geom_eco +
coord_sf(xlim = range(dat$Lon_i), ylim = range(dat$Lat_i)) +
geom_jitter(shape = 21, aes(fill = log(b_i/D_i))) +
facet_wrap(vars(t_i)) +
#facet_grid(vars(t_i), vars(season)) +
scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", fill = "Residual") +
theme_bw() +
theme(panel.spacing = unit(0, "in")) +
ggtitle(category_names[cc + 1] %>% paste(country_list[[country + 1]]))
ggsave(file.path(working_dir, paste0("residual_map_", category_names[cc + 1], "_", country_list[[country + 1]], "_ecoregion.png")),
g, height = 6, width = 8)
# DHARMa
#g <- dat_out %>%
# ggplot(aes(Lon_i, Lat_i)) +
# geom_eco +
# coord_sf(xlim = range(dat$Lon_i), ylim = range(dat$Lat_i)) +
# geom_jitter(shape = 21, aes(fill = dharma_resid)) +
# facet_wrap(vars(t_i)) +
# #facet_grid(vars(t_i), vars(season)) +
# scale_fill_gradient2(high = scales::muted("red"), midpoint = 0.5, low = scales::muted("blue")) +
# labs(x = "Longitude", y = "Latitude", fill = "DHARMa/nResidual") +
# theme_bw() +
# theme(panel.spacing = unit(0, "in")) +
# ggtitle(category_names[cc + 1] %>% paste(country_list[[country + 1]]))
#ggsave(file.path(working_dir, paste0("dharma_resid_map_", category_names[cc + 1], "_", country_list[[country + 1]], "_ecoregion.png")),
# g, height = 6, width = 8)
}
}
message("Finished data and residuals.")
}
if("factor" %in% do_plot) {
# Factor loadings - epsilon 2
lab <- c("Epsilon1", "Epsilon2", "Omega1", "Omega2")
Lvec <- c("L_epsilon1_cf", "L_epsilon2_cf", "L_omega1_cf", "L_omega2_cf")
Om <- c("Epsiloninput1_sft", "Epsiloninput2_sft", "Omegainput1_sf", "Omegainput2_sf")
covvec <- paste0("lowercov_uppercor_", c("epsilon1", "epsilon2", "omega1", "omega2"))
for(i in 1:length(Lvec)) {
Loadings <- fit$Report[[Lvec[i]]]
if(length(Loadings)) {
# Corr matrix
cov_j <- fit$Report[[covvec[i]]]
for(ii in 1:(nrow(cov_j)-1)) {
for(j in (ii+1):nrow(cov_j)) cov_j[ii, j] <- NA
}
corr_j <- cov_j %>%
cov2cor() %>%
structure(dimnames = list(Var1 = category_names, Var2 = category_names)) %>%
signif(2) %>%
reshape2::melt() %>%
filter(!is.na(value)) %>%
mutate(Var1 = factor(Var1, levels = category_names %>% rev()),
Var2 = factor(Var2, levels = category_names))
g <- corr_j %>%
ggplot(aes(Var2, Var1)) +
geom_tile(height = 1, width = 1, colour = "black", aes(fill = value), alpha = 0.5) +
geom_text(aes(label = value)) +
coord_cartesian(expand = FALSE) +
scale_fill_gradient2(low = scales::muted("blue"), high = scales::muted("red")) +
gfplot::theme_pbs() +
theme(legend.position = "none") +
labs(x = "", y = "") +
guides(fill = "none")
ggsave(file.path(working_dir, paste0(lab[i], "_corr.png")), g, height = 3, width = 4)
Psi_sjt <- fit$Report[[Om[i]]]
logkappa = unlist(fit$ParHat[c("logkappa1", "logkappa2")])[c(1, 2, 1, 2)[i]]
stopifnot(fit$data_list$Options_list$Options_vec[8] == 0)
tau = 1/(exp(logkappa) * sqrt(4 * pi))
Var_rot = rotate_factors(L_pj = Loadings,
Psi_sjt = Psi_sjt/tau,
RotationMethod = "Varimax",
testcutoff = 1e-4,
quiet = TRUE)
# Loadings
L_rot <- Var_rot$L_pj_rot %>% structure(dimnames = list(Category = category_names, Factor = 1:ncol(.))) %>%
reshape2::melt() %>%
mutate(Category = factor(Category, levels = rev(category_names)))
g <- L_rot %>%
ggplot(aes(factor(Factor), Category)) +
geom_tile(aes(fill = value), alpha = 0.7) +
geom_text(aes(label = round(value, 2))) +
scale_fill_viridis_c() +
coord_cartesian(expand = FALSE) +
guides(fill = "none") +
theme(legend.position = "bottom") +
labs(x = "Factor", y = "Species", fill = expression("Loadings"~psi*minute))
ggsave(file.path(working_dir, paste0(lab[i], "_Loadings.png")), g, height = 3, width = 4)
# Factors
Psi_rot <- Var_rot$Psi_rot %>%
structure(dimnames = list(Cell = 1:dim(.)[1], Factor = 1:dim(.)[2],
Year = if(i < 3) {
fit$year_labels
} else 1)) %>%
reshape2::melt() %>%
left_join(fit$spatial_list$latlon_s %>% as.data.frame() %>% mutate(Cell = 1:nrow(.)), by = "Cell")
Psi_interp <- local({
# Interpolation should be in UTM in the model
coord_out <- expand.grid(xo = seq(min(Psi_rot$Lon), max(Psi_rot$Lon), by = 1),
yo = seq(min(Psi_rot$Lat), max(Psi_rot$Lat), by = 1))
full <- lapply(unique(Psi_rot$Factor), function(ff) {
lapply(unique(Psi_rot$Year), function(yy) {
mesh_x <- filter(Psi_rot, Factor == ff, Year == yy)
interp::interp(x = mesh_x$Lon,
y = mesh_x$Lat,
z = mesh_x$value,
xo = coord_out$xo,
yo = coord_out$yo,
output = "points") %>%
bind_cols() %>% mutate(Factor = ff, Year = yy)
}) %>% bind_rows()
}) %>% bind_rows()
xy_ind <- sf::st_intersects(full %>% sf::st_as_sf(coords = c("x", "y"), crs = 4326),
ecoregion_shp %>% as("sf")) %>%
sapply(function(x) length(x) > 0)
full[xy_ind, ]
})
if(length(unique(Psi_rot$Year)) > 1) {
for(ff in 1:max(Psi_rot$Factor)) {
g <- Psi_rot %>%
filter(Factor == ff) %>%
ggplot(aes(Lon, Lat)) +
geom_eco +
coord_sf(xlim = range(Psi_rot$Lon), ylim = range(Psi_rot$Lat)) +
geom_point(shape = 21, aes(fill = value)) +
facet_wrap(vars(Year)) +
scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", colour = paste("Factor", ff)) +
theme_bw() +
theme(panel.spacing = unit(0, "in"))
ggsave(file.path(working_dir, paste0(lab[i], "_Factor_", ff, "_ecoregion.png")), g, height = 6, width = 8)
g <- Psi_interp %>%
filter(Factor == ff) %>%
ggplot(aes(x, y)) +
geom_tile(aes(fill = z, colour = z)) +
geom_eco +
coord_sf(xlim = range(Psi_rot$Lon), ylim = range(Psi_rot$Lat)) +
facet_wrap(vars(Year)) +
scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
scale_colour_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", colour = paste("Factor", ff)) +
theme_bw() +
theme(panel.spacing = unit(0, "in")) +
guides(fill = "none")
ggsave(file.path(working_dir, paste0(lab[i], "_Factor_", ff, "_interp_ecoregion.png")), g, height = 6, width = 8)
}
} else {
g <- Psi_rot %>%
ggplot(aes(Lon, Lat)) +
geom_eco +
coord_sf(xlim = range(Psi_rot$Lon), ylim = range(Psi_rot$Lat)) +
geom_point(shape = 21, aes(fill = value)) +
facet_wrap(vars(paste("Factor", Factor))) +
scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", colour = "Value") +
theme_bw() +
theme(panel.spacing = unit(0, "in"))
ggsave(file.path(working_dir, paste0(lab[i], "_Factors_ecoregion.png")), g, height = 6, width = 8)
g <- Psi_interp %>%
ggplot(aes(x, y)) +
geom_tile(aes(fill = z, colour = z)) +
geom_eco +
coord_sf(xlim = range(Psi_rot$Lon), ylim = range(Psi_rot$Lat)) +
facet_wrap(vars(paste("Factor", Factor))) +
scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
scale_colour_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", colour = "Value") +
theme_bw() +
theme(panel.spacing = unit(0, "in")) +
guides(fill = "none")
ggsave(file.path(working_dir, paste0(lab[i], "_Factors_interp_ecoregion.png")), g, height = 6, width = 8)
}
}
}
message("Plotted factors.")
}
# Plot loadings x factors (by species, year)
if("density" %in% do_plot) {
D_gct <- local({
x <- array(dim = dim(fit$Report$D_gct))
x[] <- fit$Report$D_gct
dimnames(x) <- list(Cell = 1:dim(x)[1], Category = dimnames(fit$Report$D_gct)[[2]],
Year = dimnames(fit$Report$D_gct)[[3]])
reshape2::melt(x) %>%
left_join(fit$spatial_list$latlon_g %>% as.data.frame() %>% mutate(Cell = 1:nrow(.)), by = "Cell") %>%
left_join(fit$spatial_list$loc_g %>% as.data.frame() %>% mutate(Cell = 1:nrow(.)), by = "Cell")
})
g <- D_gct %>%
dplyr::filter(Year == max(D_gct$Year)) %>%
ggplot(aes(Lon, Lat)) +
geom_eco +
coord_sf(xlim = range(D_gct$Lon), ylim = range(D_gct$Lat)) +
geom_point(shape = 21, aes(fill = log(value))) +
facet_wrap(vars(Category)) +
scale_fill_viridis_c() +
#scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", fill = "Log-Density") +
theme_bw() +
theme(panel.spacing = unit(0, "in"))
ggsave(file.path(working_dir, "Density_ecoregion.png"), g, height = 6, width = 8)
D_gct_interp <- local({
# Interpolation should be in UTM in the model
coord_out <- expand.grid(xo = seq(min(D_gct$Lon), max(D_gct$Lon), by = 1),
yo = seq(min(D_gct$Lat), max(D_gct$Lat), by = 1))
full <- lapply(unique(D_gct$Category), function(ss) {
lapply(unique(D_gct$Year), function(yy) {
mesh_x <- filter(D_gct, Category == ss, Year == yy)
interp::interp(x = mesh_x$Lon,
y = mesh_x$Lat,
z = mesh_x$value,
xo = coord_out$xo,
yo = coord_out$yo,
output = "points") %>%
bind_cols() %>% mutate(Category = ss, Year = yy)
}) %>% bind_rows()
}) %>% bind_rows()
xy_ind <- sf::st_intersects(full %>% sf::st_as_sf(coords = c("x", "y"), crs = 4326),
ecoregion_shp %>% as("sf")) %>%
sapply(function(x) length(x) > 0)
full[xy_ind, ]
})
g <- D_gct_interp %>%
dplyr::filter(Year == max(D_gct$Year), !is.na(z), z > 0) %>%
ggplot(aes(x, y)) +
geom_tile(aes(fill = log(z), colour = log(z))) +
geom_eco +
coord_sf(xlim = range(D_gct$Lon), ylim = range(D_gct$Lat)) +
facet_wrap(vars(Category)) +
scale_fill_viridis_c() +
scale_colour_viridis_c() +
#scale_fill_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
#scale_colour_gradient2(high = scales::muted("red"), low = scales::muted("blue")) +
labs(x = "Longitude", y = "Latitude", colour = "Log Density", fill = "Log Density") +
theme_bw() +
theme(panel.spacing = unit(0, "in"))
ggsave(file.path(working_dir, "Density_interp_ecoregion.png"), g, height = 6, width = 8)
}
invisible()
}