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3. analysis.R
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#### R script originally prepared by Nicholas J Clark ([email protected]) ####
#### Analyse candidate models and produce model-based visualisations ####
library(mvgam)
library(scoringRules)
# Load the pre-prepared modelling data and visualisation functions
load('data/rodents_data_tsobjects.rda')
source('Functions/checking_functions.R')
# Load all relevant models
load('Outputs/bench1.rda')
load('Outputs/bench1.2.rda')
load('Outputs/bench2.rda')
load('Outputs/modvar.rda')
load('Outputs/bench1_all.rda')
load('Outputs/bench1.2_all.rda')
load('Outputs/bench2_all.rda')
load('Outputs/modvar_all.rda')
#### Comparisons among models using in-sample and out-of-sample
# fit metrics ####
# In-sample 'hypothesis test' using LOO
loo_compare(modvar_all, bench1_all, bench1.2_all, bench2_all)
loo1 <- loo(modvar_all, save_psis = TRUE)
plot(loo1)
# GAM-VAR performs best
# Load the exact leave-future-out cross-validation results and plot them
load('Outputs/roll_evaluations.rda')
# bench1 and the non-hierarchical bench1.2 are fairly similar in
# performance overall
bench1_tot <- unlist(lapply(1:5, function(x){
sum(exp(bench1_roll[[x]]$cmbn_score$score), na.rm = TRUE)
}))
bench1.2_tot <- unlist(lapply(1:5, function(x){
sum(exp(bench1.2_roll[[x]]$cmbn_score$score), na.rm = TRUE)
}))
length(which(bench1_tot < bench1.2_tot))
# We therefore only make plots for the three main models
# (modvar, bench1, bench2) to improve clarity of plots
scores_array <- array(NA, dim = c(3, 6, 12))
tot_scores <- matrix(NA, nrow = 3, ncol = 6)
jpeg('Figures/variogram_scores.jpg', width = 6.25, height = 4.25,
res = 300, units = 'in')
layout(matrix(1:6, ncol = 3, byrow = TRUE))
par(mar = c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
for(i in 1:6){
if(i == 6){
# If this is the primary model fitted, we need to extract
# forecast scores
bench1_scores <- exp(log(score(forecast(bench1), score = 'variogram',
log = TRUE)$all_series$score[1:12] *
score(forecast(bench1), score = 'energy',
log = TRUE)$all_series$score[1:12]))
bench2_scores <- exp(log(score(forecast(bench2), score = 'variogram',
log = TRUE)$all_series$score[1:12] *
score(forecast(bench2), score = 'energy',
log = TRUE)$all_series$score[1:12]))
modvar_scores <- exp(log(score(forecast(modvar), score = 'variogram',
log = TRUE)$all_series$score[1:12] *
score(forecast(modvar), score = 'energy',
log = TRUE)$all_series$score[1:12]))
} else {
# Other scores are already calculated
bench1_scores <- exp(bench1_roll[[i]]$cmbn_score$score)
bench2_scores <- exp(bench2_roll[[i]]$cmbn_score$score)
modvar_scores <- exp(modvar_roll[[i]]$cmbn_score$score)
}
# Compute total scores
tot_scores[1,i] <- sum(bench1_scores, na.rm = TRUE)
tot_scores[2,i] <- sum(bench2_scores, na.rm = TRUE)
tot_scores[3,i] <- sum(modvar_scores, na.rm = TRUE)
# Save score data into an array for storing
scores_array[1,i,] <- log(bench1_scores)
scores_array[2,i,] <- log(bench2_scores)
scores_array[3,i,] <- log(modvar_scores)
# Create loess smooth lines
bench1_lines <- loess_scores(bench1_scores)
bench2_lines <- loess_scores(bench2_scores)
modvar_lines <- loess_scores(modvar_scores)
plot(1, type = "n", bty = 'L',
ylab = '',
ylim = c(1.6, 8),
xlim = c(1, length(bench1_scores)),
xlab = '',
xaxt = 'n',
yaxt = 'n',
main = bquote(italic(T)~' = '~.(ifelse(i <= 5, evaluation_seq[i],
273))))
if(i > 3){
axis(side = 1)
} else {
axis(side = 1, labels = NA)
}
if(i %in% c(1, 4)){
axis(side = 2)
} else {
axis(side = 2, labels = NA)
}
lines(bench1_lines, lwd = 3.25,
col = 'white')
lines(bench1_lines, lwd = 2.9,
col = "#8F2727")
lines(bench2_lines, lwd = 3.25,
col = 'white')
lines(bench2_lines, lwd = 2.9,
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5])
lines(modvar_lines, lwd = 3.25,
col = 'white')
lines(modvar_lines, lwd = 2.9)
points(log(bench1_scores), pch = 16, cex = 0.9,
col = 'white')
points(log(bench1_scores), pch = 16, cex = 0.7,
col = "#8F2727")
points(log(bench2_scores), pch = 16, cex = 0.9,
col = 'white')
points(log(bench2_scores), pch = 16, cex = 0.7,
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5])
points(log(modvar_scores), pch = 16, cex = 0.9,
col = 'white')
points(log(modvar_scores), pch = 16, cex = 0.7)
box(bty = 'l', lwd = 2)
if(i == 6){
text(x = 3.7, y = bench2_lines[3] - 0.4, labels = 'AR',
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5], cex = 1,
adj = 1)
text(x = 4.5, y = bench1_lines[3] + 0.4, labels = 'GAM-AR',
col = "#8F2727", cex = 1, adj = 1)
text(x = 5, y = modvar_lines[3] - 0.5, labels = 'GAM-VAR',
cex = 1, adj = 1)
}
}
mtext('Forecast horizon (lunar months)', side = 1, outer = TRUE,
line = 0.25, cex = 0.9)
mtext('Weighted variogram score', side = 2, outer = TRUE,
line = 0.3, cex = 0.9)
dev.off()
# Get the score data into a data.frame format
reshape2::melt(scores_array,
varnames = c('model', 'end_train',
'horizon'),
value.name = 'score') %>%
dplyr::mutate(model = dplyr::case_when(
model == 1 ~ 'AR',
model == 2 ~ 'GAM-AR',
model == 3 ~ 'GAM-VAR'
),
end_train = dplyr::case_when(
end_train == 1 ~ 75,
end_train == 2 ~ 115,
end_train == 3 ~ 154,
end_train == 4 ~ 194,
end_train == 5 ~ 233,
end_train == 6 ~ 273)) -> scores_df
save(scores_df, file = 'Outputs/scores_df.rda')
# GAM-VAR (row 3) provides lowest overall scores in four of six cv folds
apply(tot_scores, 2, which.min)
# GAM-VAR is superior
# Look at a few unconstrained forecasts and calculate univariate
# Discrete Rank Probability Scores
plot(bench1, 'forecast', series = 1, newdata = data_test)
plot(bench2, 'forecast', series = 1, newdata = data_test)
plot(modvar, 'forecast', series = 1, newdata = data_test)
plot(bench1, 'forecast', series = 2, newdata = data_test)
plot(bench2, 'forecast', series = 2, newdata = data_test)
plot(modvar, 'forecast', series = 2, newdata = data_test)
plot(bench1, 'forecast', series = 3, newdata = data_test)
plot(bench2, 'forecast', series = 3, newdata = data_test)
plot(modvar, 'forecast', series = 3, newdata = data_test)
plot(bench1, 'forecast', series = 4, newdata = data_test)
plot(bench2, 'forecast', series = 4, newdata = data_test)
plot(modvar, 'forecast', series = 4, newdata = data_test)
plot(bench1, 'forecast', series = 5, newdata = data_test)
plot(bench2, 'forecast', series = 5, newdata = data_test)
plot(modvar, 'forecast', series = 5, newdata = data_test)
plot(bench1, 'forecast', series = 6, newdata = data_test)
plot(bench2, 'forecast', series = 6, newdata = data_test)
plot(modvar, 'forecast', series = 6, newdata = data_test)
plot(bench1, 'forecast', series = 7, newdata = data_test)
plot(bench2, 'forecast', series = 7, newdata = data_test)
plot(modvar, 'forecast', series = 7, newdata = data_test)
plot(bench1, 'forecast', series = 8, newdata = data_test)
plot(bench2, 'forecast', series = 8, newdata = data_test)
plot(modvar, 'forecast', series = 8, newdata = data_test)
plot(bench1, 'forecast', series = 9, newdata = data_test)
plot(bench2, 'forecast', series = 9, newdata = data_test)
plot(modvar, 'forecast', series = 9, newdata = data_test)
# Inspect process error estimates for the various models and compare predictions
jpeg('Figures/trend_sigmas.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 0, 0, 0))
modvar_all_sigmas <- MCMCvis::MCMCchains(modvar$model_output,
'Sigma')
names_org <- expand.grid(as.character(1:9),
as.character(1:9))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
bench1_sigmas <- MCMCvis::MCMCchains(bench1$model_output,
paste0('sigma[', x, ']'),
ISB = FALSE)
bench2_sigmas <- MCMCvis::MCMCchains(bench2$model_output,
paste0('sigma[', x, ']'),
ISB = FALSE)
modvar_sigmas <- sqrt(modvar_all_sigmas[,
which(names_org[,1] == x &
names_org[,2] == x)])
break_seq <- seq(0, 1, length.out = 50)
hist_data <- hist(bench1_sigmas,
breaks = break_seq,
plot = F)
hist_data2 <- hist(bench2_sigmas,
breaks = break_seq,
plot = F)
hist_data3 <- hist(modvar_sigmas,
breaks = break_seq,
plot = F)
ylimits <- range(c(hist_data$density,
hist_data2$density,
hist_data3$density))
plot(1, type = "n", bty = 'n',
ylab = '',
xlab = '',
xaxt = 'n',
yaxt = 'n',
xlim = c(0.2, 1),
ylim = ylimits * 1.2)
plot_line_hist(s = bench1_sigmas,
min_val = min(break_seq),
max_val = max(break_seq),
delta = ((max(break_seq) - min(break_seq)) /
length(break_seq)),
line_col = "#8F2727",
poly_col = scales::alpha("#8F2727",
0.35))
plot_line_hist(s = bench2_sigmas,
min_val = min(break_seq),
max_val = max(break_seq),
delta = ((max(break_seq) - min(break_seq)) /
length(break_seq)),
line_col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5],
poly_col = scales::alpha(RColorBrewer::brewer.pal(n = 5, 'Blues')[5],
0.35))
plot_line_hist(s = modvar_sigmas,
min_val = min(break_seq),
max_val = max(break_seq),
delta = ((max(break_seq) - min(break_seq)) /
length(break_seq)),
line_col = 'black',
poly_col = scales::alpha("black",
0.35))
if(x > 6 ){
axis(side = 1, lwd = 2, cex.axis = 0.8, tck= -0.08)
} else {
axis(side = 1, lwd = 2, labels = NA, tck= -0.08)
}
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x == 8){
title(xlab = expression(paste("Process standard deviation (sqrt(", Sigma['[i,i]'], "))")),
xpd = NA, line = 2.25)
}
}
dev.off()
#### Analyse and evaluate the GAM-VAR model ####
# Inspect variation in stability estimates
all_metrics <- purrr::map(modvar_roll, 'fc_metrics')
fc_uncertainty_props <- do.call(rbind.data.frame,
purrr::map(all_metrics, 'unc_props')) %>%
dplyr::bind_rows(modvar_metrics$unc_props)
dplyr::glimpse(fc_uncertainty_props)
plot_sp_unc_time(fc_uncertainty_props, series = 1)
ggsave('figures/DM_unc_prop.jpg',
units = 'in', width = 7, height = 5)
plot_sp_unc_time(fc_uncertainty_props, series = 5)
ggsave('figures/PB_unc_prop.jpg',
units = 'in', width = 7, height = 5)
plot_sp_unc_time(fc_uncertainty_props, series = 8)
ggsave('figures/PP_unc_prop.jpg',
units = 'in', width = 7, height = 5)
stab_metrics <- do.call(rbind.data.frame,
purrr::map(all_metrics, 'stab_metrics')) %>%
dplyr::bind_rows(modvar_metrics$stab_metrics)
dplyr::glimpse(stab_metrics)
library(ggbeeswarm)
p1 <- ggplot(stab_metrics, aes(y = reactivity,
x = as.factor(end_train))) +
ggbeeswarm::geom_quasirandom(shape = 21, size = 0.5,
dodge.width = .9,
color = "black",
alpha = .1,
show.legend = F) +
geom_violin(position = position_dodge(width = 0.9),
fill = 'grey30',
alpha = 0.2, draw_quantiles = TRUE, trim = TRUE,
scale = 'width',
color = NA) +
geom_boxplot(notch = TRUE,
outlier.size = -1,
color = "black",
lwd = 0.8,
alpha = 0.7) +
theme_bw() +
labs(x = '',
y = expression(log(sigma[1](A))),
title = 'Reactivity to perturbations') +
theme(legend.position = 'none',
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line(),
plot.title = element_text(size = 11))
p2 <- ggplot(stab_metrics, aes(y = sp_prop_adj,
x = as.factor(end_train))) +
ggbeeswarm::geom_quasirandom(shape = 21, size = 0.5,
dodge.width = .9,
color = "black",
alpha = .1,
show.legend = F) +
geom_violin(position = position_dodge(width = 0.9),
fill = 'grey30',
alpha = 0.2, draw_quantiles = TRUE, trim = TRUE,
scale = 'width',
color = NA) +
geom_boxplot(notch = TRUE,
outlier.size = -1,
color = "black",
lwd = 0.8,
alpha = 0.7) +
scale_y_continuous(limits = c(0.45, 1)) +
theme_bw() +
labs(x = '',
y = expression(det(A)^{2/p}),
title = "% stability due to species' interactions") +
theme(legend.position = 'none',
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line(),
plot.title = element_text(size = 11))
fc_uncertainty_props %>%
dplyr::filter(horizon < 7,
process == 'Process_error') -> perror_dat
p3 <- ggplot(perror_dat, aes(y = `Proportion of variance`,
x = as.factor(end_train))) +
ggbeeswarm::geom_quasirandom(shape = 21, size = 0.5,
dodge.width = .9,
color = "black",
alpha = .1,
show.legend = F) +
geom_violin(position = position_dodge(width = 0.9),
fill = 'grey30',
alpha = 0.2, draw_quantiles = TRUE, trim = TRUE,
scale = 'width',
color = NA) +
geom_boxplot(notch = TRUE,
outlier.size = -1,
color = "black",
lwd = 0.8,
alpha = 0.7) +
theme_bw() +
labs(x = 'Last training time (T)',
y = expression(Var(Sigma[t+1:t+6])/Var(X[t+1:t+6])),
title = expression('% uncertainty due to process error')) +
theme(legend.position = 'none',
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line(),
plot.title = element_text(size = 11))
p4 <- ggplot(stab_metrics, aes(y = intersp_interact_cont,
x = as.factor(end_train))) +
ggbeeswarm::geom_quasirandom(shape = 21, size = 0.5,
dodge.width = .9,
color = "black",
alpha = .1,
show.legend = F) +
geom_violin(position = position_dodge(width = 0.9),
fill = 'grey30',
alpha = 0.2, draw_quantiles = TRUE, trim = TRUE,
scale = 'width',
color = NA) +
geom_boxplot(notch = TRUE,
outlier.size = -1,
color = "black",
lwd = 0.8,
alpha = 0.7) +
theme_bw() +
scale_y_continuous(limits = c(0, 0.28)) +
labs(x = 'Last training time (T)',
y = expression(abs(i != j)~'for'~2 %*% det(A) %*% A^{-1}),
title = expression('Sensitivity to interspecific interactions')) +
theme(legend.position = 'none',
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line(),
plot.title = element_text(size = 11))
library(patchwork)
p1 + p2 + p3 + p4 +
plot_layout(ncol = 2)
ggsave('figures/stability_overtime.jpg',
units = 'in', width = 7, height = 5)
# Dirichlet regression to understand uncertainty components across species
# and their relationships with metrics of community stability
fc_uncertainty_props %>%
dplyr::filter(horizon == 12) %>%
dplyr::left_join(stab_metrics %>%
dplyr::select(-model_name) %>%
dplyr::group_by(end_train) %>%
# Compute posterior mean estimate for reactivity
# and other metrics
dplyr::summarise_all(mean)) %>%
dplyr::mutate(proportion = `Proportion of variance`) %>%
# No zeros allowed in Dirichlet regression
dplyr::mutate(proportion = pmax(0.000001, proportion)) %>%
dplyr::select(proportion, series, process, reactivity,
var_returnrate) %>%
tidyr::pivot_wider(id_cols = c(series, reactivity,
var_returnrate),
names_from = process,
values_from = 'proportion') %>%
# Re-normalize
dplyr::rowwise() %>%
dplyr::mutate(sum = sum(Process_error,
Interactions,
Mintemp_smooth,
NDVI_effect)) %>%
dplyr::mutate(Process_error = Process_error / sum,
Interactions = Interactions / sum,
Mintemp_smooth = Mintemp_smooth / sum,
NDVI_effect = NDVI_effect / sum) -> props_mod_dat
# Return-rates don't vary much, so we only focus on reactivity
# (which does show quite a lot of variation through time)
library(brms)
fit <- brm(bind(Process_error, Interactions,
Mintemp_smooth, NDVI_effect) ~ series +
s(reactivity, by = series, k = 4),
data = props_mod_dat, family = dirichlet(),
backend = 'cmdstanr', cores = 4)
summary(fit)
plot(conditional_effects(fit, categorical = TRUE),
plot = FALSE)[[2]] +
theme_bw() +
labs(x = 'Reactivity',
y = 'Proportion of forecast variance') +
scale_y_continuous(limits = c(0, 1)) +
theme_minimal() +
scale_fill_viridis(discrete = TRUE,
labels = c('Process_error' = 'Process error',
'Mintemp_smooth' = 'Mintemp smooth',
'NDVI_effect' = 'NDVI effect')) +
scale_colour_viridis(discrete = TRUE,
labels = c('Process_error' = 'Process error',
'Mintemp_smooth' = 'Mintemp smooth',
'NDVI_effect' = 'NDVI effect')) +
theme(legend.title = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line())
ggsave('figures/reactivity_uncprops_avg.jpg',
units = 'in', width = 4.75, height = 3.25)
conditions <- data.frame(series = unique(props_mod_dat$series))
mylabels <- species_names
names(mylabels) <- as.character(1:9)
plot(conditional_effects(fit, conditions = conditions,
categorical = TRUE),
plot = FALSE,
facet_args = list(labeller = labeller(cond__ = mylabels)))[[2]] +
theme_minimal() +
scale_fill_viridis(discrete = TRUE,
labels = c('Process_error' = 'Process error',
'Mintemp_smooth' = 'Mintemp smooth',
'NDVI_effect' = 'NDVI effect')) +
scale_colour_viridis(discrete = TRUE,
labels = c('Process_error' = 'Process error',
'Mintemp_smooth' = 'Mintemp smooth',
'NDVI_effect' = 'NDVI effect')) +
labs(x = 'Reactivity',
y = 'Proportion of forecast variance') +
scale_y_continuous(limits = c(0, 1)) +
theme(legend.title = element_blank(),
strip.text = element_text(face = "italic"),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
axis.line = element_line(color = 'black'),
axis.ticks = element_line()) +
geom_hline(aes(yintercept=-Inf)) +
geom_vline(aes(xintercept=-Inf))
ggsave('figures/reactivity_uncprops_byspecies.jpg',
units = 'in', width = 6.75, height = 4.25)
# Plot residuals for all series to look for any unmodelled
# systematic variation
jpeg('Figures/resids_time.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_resids_time(object = modvar_all, series = x)
if(x > 6){
time_axis()
} else {
time_axis(labels = FALSE)
}
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x == 4){
title(ylab = 'Randomized quantile residuals', xpd = NA, line = 2.25)
}
}
dev.off()
# Plot residual ACF functions for all series
jpeg('Figures/resid_acfs.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_mod_acfs(object = modvar_all, series = x)
if(x > 6){
axis(1, at = seq(1, 24, by = 2), tck = -0.05, cex.axis = 1)
} else {
axis(1, at = seq(1, 24, by = 2), labels = NA, tck = -0.05, cex.axis = 1)
}
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x == 4){
title(ylab = 'Residual autocorrelation function', xpd = NA, line = 2.25)
}
if(x == 8){
title(xlab = 'Lag (lunar months)',
xpd = NA, line = 2.25)
}
}
dev.off()
# Plot residual QQ normal functions for all series
jpeg('Figures/resid_qqnorms.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_mod_qq(object = modvar_all, series = x)
if(x > 6){
axis(1, tck = -0.05, cex.axis = 1)
} else {
axis(1, labels = NA, tck = -0.05, cex.axis = 1)
}
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x %in% c(1,4,7)){
axis(side = 2)
} else {
axis(side = 2, labels = NA)
}
if(x == 4){
title(ylab = 'Sample quantiles', xpd = NA, line = 2.25)
}
if(x == 8){
title(xlab = 'Theoretical quantiles',
xpd = NA, line = 2.25)
}
}
dev.off()
# Calculate expert-adjusted forecast distributions, constrained by
# the total number of available traps (196)
cons_fcs <- get_fc_constrained(modvar)
# Plot the forecasts, which will also show the Discrete Rank Probability Score
# for the out of sample period
jpeg('Figures/all_fcs.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_fc_constrained(cons_fcs, series = x, newdata = data_test,
hide_xlabels = TRUE, ylab = '')
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x %in% c(1, 4, 7)){
title(ylab = 'Posterior predictions', xpd = NA, line = 2.25)
}
if(x > 6){
time_axis()
} else {
time_axis(labels = FALSE)
}
}
dev.off()
# Plot the NDVI random slope distributions
plot(modvar_all, 're', trend_effects = TRUE)
# Plot the distributed lag posterior medians as bivariate heatmaps
jpeg('Figures/dist_lags.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:6, ncol = 3, nrow = 2, byrow = TRUE))
plot_mvgam_smooth(modvar_all, smooth = 1, trend_effects = TRUE)
plot_mvgam_smooth(modvar_all, smooth = 2, trend_effects = TRUE)
plot_mvgam_smooth(modvar_all, smooth = 3, trend_effects = TRUE)
plot_mvgam_smooth(modvar_all, smooth = 4, trend_effects = TRUE)
plot_mvgam_smooth(modvar_all, smooth = 5, trend_effects = TRUE)
mtext('Minimum temperature (scaled)', side = 1, outer = TRUE,
cex = 0.8)
mtext('Lag (in months)', side = 2, outer = TRUE,
cex = 0.8)
dev.off()
# Plot high vs low NDVI posterior contrast distributions
jpeg('Figures/NDVI_contrasts.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 0, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_ndvi_contrast(object = modvar_all, series = x,
xlimits = c(-2, 2),
xlabel = FALSE,
show_xlabs = x > 6)
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x == 8){
title(xlab = 'Expected change in captures with higher NDVI', xpd = NA, line = 2.25)
}
}
dev.off()
# Now for the no-pooling example
jpeg('Figures/NDVI_contrasts_nopool.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 0, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_ndvi_contrast(object = bench1.2_all, series = x,
xlimits = c(-6, 6),
xlabel = FALSE,
show_xlabs = x > 6)
title(main = bquote(italic(.(species_names[x]))), cex.main = 1, line = 0.35,
xpd = NA)
if(x == 8){
title(xlab = 'Expected change in captures with higher NDVI', xpd = NA, line = 2.25)
}
}
dev.off()
# Plot mintemp conditional curves
jpeg('Figures/mintemp_conditionals.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(1.5, 1.5, 1, 1),
oma = c(1.75, 1.75, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_mintemp_conditional(object = modvar_all, series = x,
xlabel = x > 6,
ylabel = x %in% c(1,4,7))
if(x == 4){
title(ylab = 'Conditional minimum temperature effect (scaled)',
xpd = NA, line = 2.25)
}
}
dev.off()
# Plot mintemp conditional curves
jpeg('Figures/mintemp_conditionals_nopool.jpeg', width = 6.25, height = 4.25,
res = 300, units = 'in')
par(mar=c(1.5, 1.5, 1, 1),
oma = c(1.75, 1.75, 0, 0))
layout(matrix(1:9, ncol = 3, nrow = 3, byrow = TRUE))
for(x in 1:9){
plot_mintemp_conditional(object = bench1.2_all, series = x,
xlabel = x > 6,
ylabel = x %in% c(1,4,7))
if(x == 4){
title(ylab = 'Conditional minimum temperature effect (scaled)',
xpd = NA, line = 2.25)
}
}
dev.off()
# Plot AR and VAR coefficients
plot_var_coefs(object = modvar_all)
# Plot variance-covariance estimates (as correlation matrices)
plot_var_cors(object = modvar_all)
# Plot some of the interesting forecast comparisons
rstan::stan_hist(modvar_all$model_output, c('A[2,4]',
'A[4,2]'))
jpeg('Figures/DO_PF_trends.jpeg', width = 6.5, height = 6.5,
res = 300, units = 'in')
par(mar=c(2.25, 4, 0.5, 1))
layout(matrix(1:3, nrow = 3, byrow = T))
plot_trend_comp(object = modvar, series1 = 2, series2 = 7,
hide_xlabels = TRUE)
time_axis(labels = FALSE)
abline(v = max(data_train$time), col = '#FFFFFF60', lwd = 2.85)
abline(v = max(data_train$time), col = 'black', lwd = 2.5, lty = 'dashed')
text(x = max(data_train$time) + 14, y = 2.05, labels = 'Testing')
arrows(x0 = max(data_train$time) + 6, x1 = max(data_train$time) + 24,
y0 = 1.72, y1 = 1.72, lwd = 1, length = 0.08)
text(x = max(data_train$time) - 15, y = 2.05, labels = 'Training')
arrows(x0 = max(data_train$time) - 6, x1 = max(data_train$time) - 24,
y0 = 1.72, y1 = 1.72, lwd = 1, length = 0.08)
text(x = 65, y = -1.45, labels = expression(italic(Dipodomys~ordii)),
col = "#8F2727", cex = 1)
text(x = 48, y = 1.8, labels = expression(italic(Perognathus~flavus)),
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5], cex = 1)
plot_fc_constrained(cons_fcs, series = 2, newdata = data_test,
hide_xlabels = TRUE,
ylab = 'Posterior predictions')
time_axis(labels = FALSE)
plot_fc_constrained(cons_fcs, series = 7, newdata = data_test,
hide_xlabels = TRUE,
colours = 'blues',
ylab = 'Posterior predictions')
time_axis()
dev.off()
# Compare the above for the three models
jpeg('Figures/DO_PF_trends_allmods.jpeg', width = 6.5, height = 6.5,
res = 300, units = 'in')
par(mar=c(2.25, 4, 0.75, 1))
layout(matrix(1:3, nrow = 3, byrow = T))
plot_trend_comp(object = modvar, series1 = 2, series2 = 7,
hide_xlabels = TRUE)
title('GAM-VAR', adj = 0)
time_axis(labels = FALSE)
abline(v = max(data_train$time), col = '#FFFFFF60', lwd = 2.85)
abline(v = max(data_train$time), col = 'black', lwd = 2.5, lty = 'dashed')
text(x = max(data_train$time) + 14, y = 2.05, labels = 'Testing')
arrows(x0 = max(data_train$time) + 6, x1 = max(data_train$time) + 24,
y0 = 1.72, y1 = 1.72, lwd = 1, length = 0.08)
text(x = max(data_train$time) - 15, y = 2.05, labels = 'Training')
arrows(x0 = max(data_train$time) - 6, x1 = max(data_train$time) - 24,
y0 = 1.72, y1 = 1.72, lwd = 1, length = 0.08)
text(x = 65, y = -1.25, labels = expression(italic(Dipodomys~ordii)),
col = "#8F2727", cex = 1)
text(x = 48, y = 1.8, labels = expression(italic(Perognathus~flavus)),
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5], cex = 1)
plot_trend_comp(object = bench1, series1 = 2, series2 = 7,
hide_xlabels = TRUE)
title('GAM-AR', adj = 0)
time_axis(labels = FALSE)
abline(v = max(data_train$time), col = '#FFFFFF60', lwd = 2.85)
abline(v = max(data_train$time), col = 'black', lwd = 2.5, lty = 'dashed')
plot_trend_comp(object = bench2, series1 = 2, series2 = 7,
hide_xlabels = TRUE)
title('AR', adj = 0)
time_axis()
abline(v = max(data_train$time), col = '#FFFFFF60', lwd = 2.85)
abline(v = max(data_train$time), col = 'black', lwd = 2.5, lty = 'dashed')
dev.off()
# Calculate Generalized Impulse Response Functions
all_irfs <- calc_irfs(modvar_all)
# Plot impulse response functions for select species
plot_impulse_responses(all_irfs = all_irfs,
series = 1,
filepath = 'Figures/DM_imp_response.jpg')
plot_impulse_responses(all_irfs = all_irfs,
series = 2,
filepath = 'Figures/DO_imp_response.jpg')
plot_impulse_responses(all_irfs = all_irfs,
series = 5,
filepath = 'Figures/PB_imp_response.jpg')
plot_impulse_responses(all_irfs = all_irfs,
series = 8,
filepath = 'Figures/PP_imp_response.jpg')
# Plot variance decompositions for select species
var_decomps <- calc_vardecomps(object = modvar_all)
# Inspect variance decompositions for a few species in more detail
jpeg('Figures/var_decomps.jpg', width = 6.25, height = 3.2,
res = 300, units = 'in')
par(mar=c(2, 2, 1, 1),
oma = c(1.25, 1.25, 0, 0))
layout(matrix(1:2, ncol = 2, nrow = 1, byrow = TRUE))
series = 7
all_creds <- matrix(NA, nrow = 9, ncol = 12)
for(i in 1:9){
all_creds[i,] <- get_decomp_creds(var_decomps, series, i)[5,1:12]
}
pred_vals <- 1:12
plot(1, type = "n", bty = 'L',
xlab = 'Forecast horizon (lunar months)',
xaxt = 'n',
ylab = '',
xlim = c(1, 12),
ylim = range(all_creds))
cols <- rep('grey80', 9)
cols[4] <- "#8F2727"
cols[8] <- RColorBrewer::brewer.pal(n = 5, 'Blues')[5]
for(i in 1:9){
lines(pred_vals, all_creds[i, ], col = 'white', lwd = 1.5)
lines(pred_vals, all_creds[i, ], col = cols[i], lwd = 1)
}
lines(pred_vals, all_creds[series, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[series, ], col = 'black', lwd = 3)
lines(pred_vals, all_creds[3, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[3, ], col = cols[4], lwd = 3)
lines(pred_vals, all_creds[7, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[7, ], col = cols[8], lwd = 3)
text(x = 5.10, y = .34, labels = bquote(italic(.(abbrev_names[3]))),
col = "#8F2727", cex = 1)
text(x = 3.50, y = .12, labels = bquote(italic(.(abbrev_names[7]))),
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5], cex = 1)
box(bty = 'L', lwd = 2)
title(main = bquote(italic(.(species_names[series]))), cex.main = 1, line = 0.35,
xpd = NA)
axis(1, cex.axis = 1,
tck= -0.05)
# Series 6
series = 6
all_creds <- matrix(NA, nrow = 9, ncol = 12)
for(i in 1:9){
all_creds[i,] <- get_decomp_creds(var_decomps, series, i)[5,1:12]
}
pred_vals <- 1:12
plot(1, type = "n", bty = 'L',
xlab = 'Forecast horizon (lunar months)',
xaxt = 'n',
yaxt = 'n',
ylab = '',
xlim = c(1, 12),
ylim = c(0, 1))
cols <- rep('grey80', 9)
cols[4] <- "#8F2727"
cols[5] <- RColorBrewer::brewer.pal(n = 5, 'Blues')[5]
for(i in 1:9){
lines(pred_vals, all_creds[i, ], col = 'white', lwd = 1.5)
lines(pred_vals, all_creds[i, ], col = cols[i], lwd = 1)
}
lines(pred_vals, all_creds[series, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[series, ], col = 'black', lwd = 3)
lines(pred_vals, all_creds[3, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[3, ], col = cols[4], lwd = 3)
lines(pred_vals, all_creds[6, ], col = 'white', lwd = 3.5)
lines(pred_vals, all_creds[6, ], col = cols[5], lwd = 3)
text(x = 3.1, y = .155, labels = bquote(italic(.(abbrev_names[3]))),
col = "#8F2727", cex = 1)
text(x = 4, y = .082, labels = bquote(italic(.(abbrev_names[6]))),
col = RColorBrewer::brewer.pal(n = 5, 'Blues')[5], cex = 1)
box(bty = 'L', lwd = 2)
title(main = bquote(italic(.(species_names[series]))), cex.main = 1, line = 0.35,
xpd = NA)
axis(1, cex.axis = 1,
tck= -0.05)
axis(2, labels = NA)
mtext('Forecast horizon (lunar months)', side = 1, outer = TRUE,
line = 0.25, cex = 0.9)
mtext('Proportional contribution to trend variance', side = 2, outer = TRUE,
line = 0.3, cex = 0.9)
dev.off()
#### A final figure to illustrate changes in community composition over time ####
points_circle <- function(n = 196, alpha = 2,
geometry = 'planar',
med_counts) {
radius <- function(k,n,b) {
ifelse(
k > n-b,
1,
sqrt(k-1/2)/sqrt(n-(b+1)/2)
)
}
b <- round(alpha*sqrt(n))
phi <- (sqrt(5)+1)/2
r <- radius(1:n,n,b)
theta <- 1:n * ifelse(geometry[1] == 'geodesic', 360*phi, 2*pi/phi^2)
points <- data.frame(x = r*cos(theta),
y = r*sin(theta)) %>%
dplyr::arrange(x, y)
point_vals <- sort(factor(sample(med_counts$series, size = n, replace = TRUE,
prob = med_counts$med_count),
levels = levels(data_train$series)))
cols <- RColorBrewer::brewer.pal(9, 'Paired')
plot(points, pch = 16, bty = 'none', xaxt = 'n', yaxt = 'n',
ylab = '', xlab = '',
col = cols[as.numeric(point_vals)])
}
# Grab posterior median predictions for specific timepoints and plot
jpeg('Figures/med_community_comps.jpg', width = 6.25, height = 2.85,
res = 300, units = 'in')
par(mar=c(1, 1, 1.5, 1),
oma = c(0, 0, 0, 0))
layout(matrix(1:8, nrow = 2, byrow = TRUE))
modvar_mus <- MCMCvis::MCMCchains(modvar_all$model_output, 'mus')
for(i in seq(1999, 2022, by = 3)){
print(i)
index <- min(unique(data_all$time[which(data_all$year == i & data_all$month == 7)]))
med_counts = data.frame(series = levels(data_all$series),
med_count = unlist(lapply(seq_len(length(levels(data_all$series))), function(x){
exp(quantile(modvar_mus[,seq(x,dim(modvar_mus)[2],
by = length(levels(data_all$series)))][,index], probs = 0.5))
})))
points_circle(med_counts = med_counts, n = 250)
title(main = paste0(i-2, ' - ', i), cex.main = 0.85, line = 0.95,
xpd = NA)
if(i == 1999){
mtext(bquote(italic(.("D. mirriami"))*phantom(.(" dominates"))),
col=RColorBrewer::brewer.pal(9, 'Paired')[1], adj=0.5, cex = 0.5,
line = 0.1)
mtext(bquote(phantom(italic(.("D. mirriami")))*" dominates"), adj=0.5, cex = 0.5,
line = 0.1)
}
if(i == 2002){
mtext(bquote(italic(.("C. baileyi"))*phantom(.(" begins reshuffling community"))),
col=RColorBrewer::brewer.pal(9, 'Paired')[5], adj=0.5, cex = 0.5,
line = 0.1)
mtext(bquote(phantom(italic(.("C. baileyi")))*" begins reshuffling community"), adj=0.5, cex = 0.5,
line = 0.1)
}
if(i == 2005){