Direct-estimate CAR-time model with spatially varying TCC
Area-response smoothing with a spatially varying covariate effect
Source:vignettes/wa-direct-car-time-scaled-variance-tcc-svc.qmd
1 Purpose
This article extends the scaled-variance direct-estimate CAR-time model by allowing the county mean tree canopy cover (TCC) effect to vary spatially. The response is a county-year direct estimate of live aboveground biomass density, and the inferential target remains the latent county-year biomass mean.
This is an area-response model: each row represents a county-year, not a plot. It follows the Fay-Herriot small-area model structure (Fay and Herriot 1979), with a CAR-time process replacing an independent area effect. The TCC covariate enters the latent mean model through a global slope and a county-level CAR spatially varying coefficient, while the supplied direct-estimate variances remain a variance template that the model can recalibrate.
Source code and data links for reproducing this article are collected below.
2 Model
Let \(i\) index an observed county-year direct estimate, with county \(a_i\) and year \(t_i\). The direct estimate \(\hat{y}_i\) is modeled as
\[ \hat{y}_i = \theta_i + e_i,\qquad e_i \sim N(0,\tau_i^2), \]
where \(\theta_i = \theta_{a_i,t_i}\) is the latent county-year biomass mean and \(\tau_i^2\) is the observation variance for the direct estimate. The latent mean model is
\[ \theta_i = \beta_0 + \{\beta_1 + b_{a_i}\}x_i + w_{a_i,t_i}, \]
where \(x_i\) is standardized county mean TCC, \(b_a\) is a county-level CAR deviation from the global TCC slope, and \(w_{a,t}\) is a separable CAR-time process over counties and years. Both CAR components use the county graph; the CAR-time term also uses the annual support.
The supplied direct-estimate variance \(\hat{v}_i\) enters through a scaled residual variance model. With effective sample size \(n_i\) and fixed shrinkage constant \(c = 10\), the observation variance is
\[ \log(\tau_i^2) = \log(\kappa) + \omega_i\log(\hat{v}_i) + (1 - \omega_i)\log(\tau_0^2), \qquad \omega_i = \frac{n_i}{n_i + c}. \]
The positive scaling parameter \(\kappa\) lets the model learn whether the supplied direct-estimate variances are too small or too large overall. The common scale \(\tau_0^2\) gives low-sample-size direct estimates a place to shrink toward on the log-variance scale.
Some multi-plot county-years have zero raw direct-estimate variance because all sampled plots have zero biomass. Those rows contain useful information, but a zero observation variance would treat the direct estimate as exact. The data bundle therefore assigns them a small positive floor, equal to the 5th percentile of the positive raw direct-estimate variances. In this article the floor is only a variance template: the scaled residual variance model recalibrates it using \(\kappa\), \(\tau_0^2\), and \(n_i\).
3 Data
The data chunk below reads the county polygons and county-year direct estimates. Rows with missing direct_biomass_model are retained because they define county-year latent support for recovery and fitted values, but they do not contribute to the likelihood.
Show data-reading code
wa_counties <- read_rds(file.path(data_dir, "wa_counties.rds"))
direct_estimates <- read_csv(file.path(data_dir, "wa_direct_estimates.csv"), show_col_types = FALSE)
wa_counties <- wa_counties |>
mutate(county_fips = as.character(county_fips))
direct_estimates <- direct_estimates |>
mutate(
county_fips = as.character(county_fips),
year = as.integer(year),
county_mean_tcc_scaled = as.numeric(scale(county_mean_tcc))
) |>
arrange(county_fips, year)
observed_direct <- direct_estimates |>
filter(!is.na(direct_biomass_model))The table summarizes the area-response data used in this article. County-years with a single FIA plot remain in the county-year support, but they are not used as direct-estimate likelihood rows because their design-based variance cannot be estimated from one plot.
Show data summary code
direct_summary <- tibble(
quantity = c(
"counties",
"years",
"county-years",
"model-ready direct estimates",
"missing model responses",
"single-plot rows retained but excluded",
"variance-floor rows",
"median plot count among modeled county-years",
"median direct-estimate SE"
),
value = c(
n_distinct(direct_estimates$county_fips),
n_distinct(direct_estimates$year),
nrow(direct_estimates),
sum(direct_estimates$direct_estimate_in_model),
sum(is.na(direct_estimates$direct_biomass_model)),
sum(direct_estimates$direct_estimate_status == "single_plot_no_variance"),
sum(direct_estimates$direct_estimate_in_model &
direct_estimates$direct_biomass_vhat_source == "floor", na.rm = TRUE),
median(observed_direct$n, na.rm = TRUE),
median(observed_direct$direct_biomass_se_model, na.rm = TRUE)
)
) |>
mutate(value = fmt_num(as.numeric(value), 1))
show_table(direct_summary, caption = "County-year direct-estimate data used in the scaled-variance CAR-time SVC model.")| quantity | value |
|---|---|
| counties | 39.0 |
| years | 10.0 |
| county-years | 390.0 |
| model-ready direct estimates | 315.0 |
| missing model responses | 75.0 |
| single-plot rows retained but excluded | 15.0 |
| variance-floor rows | 60.0 |
| median plot count among modeled county-years | 19.0 |
| median direct-estimate SE | 23.6 |
The next table shows selected-year data coverage. The model includes all counties in every year, but years with fewer observed direct estimates rely more heavily on the model structure.
Show year coverage code
year_coverage <- direct_estimates |>
group_by(year) |>
summarise(
counties_with_direct_estimates = sum(direct_estimate_in_model),
median_plot_n = median(n[n > 0], na.rm = TRUE),
median_direct_biomass = median(direct_biomass_model, na.rm = TRUE),
median_direct_se = median(direct_biomass_se_model, na.rm = TRUE),
.groups = "drop"
)
show_table(
year_coverage |>
filter(year %in% c(2016, 2018, 2020, 2022, 2024, 2025)) |>
mutate(
median_plot_n = fmt_num(median_plot_n, 0),
median_direct_biomass = fmt_num(median_direct_biomass, 1),
median_direct_se = fmt_num(median_direct_se, 1)
),
caption = "Selected-year direct-estimate coverage and precision."
)| year | counties_with_direct_estimates | median_plot_n | median_direct_biomass | median_direct_se |
|---|---|---|---|---|
| 2016 | 39 | 22 | 90.3 | 23.6 |
| 2018 | 38 | 22 | 84.1 | 22.2 |
| 2020 | 36 | 18 | 51.8 | 17.9 |
| 2022 | 37 | 21 | 72.7 | 12.9 |
| 2024 | 18 | 3 | 136.3 | 75.4 |
| 2025 | 0 | NA | NA | NA |
4 County Mean TCC
County mean TCC is computed from the annual USDA Forest Service Tree Canopy Cover raster products for each county-year (USDA Forest Service 2026). The model uses a standardized version of this covariate so the global TCC coefficient and county-specific deviations are measured per one standard deviation increase in county mean TCC.
Show TCC summary code
tcc_summary <- direct_estimates |>
summarise(
mean_tcc = mean(county_mean_tcc, na.rm = TRUE),
sd_tcc = sd(county_mean_tcc, na.rm = TRUE),
min_tcc = min(county_mean_tcc, na.rm = TRUE),
max_tcc = max(county_mean_tcc, na.rm = TRUE),
observed_cor = cor(direct_biomass_model, county_mean_tcc, use = "complete.obs")
) |>
pivot_longer(everything(), names_to = "quantity", values_to = "value") |>
mutate(
quantity = recode(
quantity,
mean_tcc = "mean county TCC",
sd_tcc = "SD county TCC",
min_tcc = "minimum county TCC",
max_tcc = "maximum county TCC",
observed_cor = "correlation with direct biomass"
),
value = fmt_num(value, 2)
)
show_table(tcc_summary, caption = "County-year TCC summary.")| quantity | value |
|---|---|
| mean county TCC | 38.15 |
| SD county TCC | 23.71 |
| minimum county TCC | 1.84 |
| maximum county TCC | 70.75 |
| correlation with direct biomass | 0.80 |
The map shows the spatial pattern in county mean TCC for one representative year. The SVC model below asks whether the biomass-TCC association also varies over this county graph.
Show TCC map code
tcc_map_year <- 2024
tcc_map_dat <- wa_counties |>
left_join(
direct_estimates |>
filter(year == tcc_map_year) |>
select(county_fips, county_mean_tcc),
by = "county_fips"
)
ggplot(tcc_map_dat) +
geom_sf(aes(fill = county_mean_tcc), color = "grey80", linewidth = 0.12) +
coord_sf(expand = FALSE) +
scale_fill_gradientn(
colors = stlmm_palette(),
name = "TCC (%)"
) +
labs(title = paste("County mean TCC,", tcc_map_year))
5 County Graph
The county graph defines spatial neighbors for the CAR component. Washington has island components, so the graph construction explicitly adds nearest-neighbor bridge edges. The island rule is part of the model definition.
g <- car_graph(
wa_counties,
id = "county_fips",
island = "nearest",
island_k = 4
)
g$island_added_edges from to distance
1 53055 53029 56295.37
2 53055 53035 100453.91
3 53055 53073 100817.65
4 53055 53057 102288.11The figure checks the graph visually. Thin lines connect neighboring county centroids; thicker colored lines show bridge edges added for island components.
Show graph plotting code
coord_dat <- data.frame(
county_fips = wa_counties$county_fips,
sf::st_coordinates(sf::st_point_on_surface(sf::st_geometry(wa_counties)))
)
edge_index <- which(as.matrix(g$adjacency) != 0, arr.ind = TRUE)
edge_index <- edge_index[edge_index[, "row"] < edge_index[, "col"], , drop = FALSE]
edge_dat <- tibble(
from = g$ids[edge_index[, "row"]],
to = g$ids[edge_index[, "col"]]
) |>
mutate(
key = paste(pmin(from, to), pmax(from, to), sep = "--"),
x = coord_dat$X[match(from, coord_dat$county_fips)],
y = coord_dat$Y[match(from, coord_dat$county_fips)],
xend = coord_dat$X[match(to, coord_dat$county_fips)],
yend = coord_dat$Y[match(to, coord_dat$county_fips)]
)
island_key <- paste(
pmin(g$island_added_edges$from, g$island_added_edges$to),
pmax(g$island_added_edges$from, g$island_added_edges$to),
sep = "--"
)
edge_dat$island <- edge_dat$key %in% island_key
ggplot(wa_counties) +
geom_sf(fill = "grey96", color = "white", linewidth = 0.15) +
geom_segment(
data = edge_dat,
aes(x = x, y = y, xend = xend, yend = yend, color = island, linewidth = island)
) +
geom_point(data = coord_dat, aes(X, Y), color = stlmm_color("primary"), size = 1.3) +
coord_sf(expand = FALSE) +
scale_color_manual(
values = c("FALSE" = "grey45", "TRUE" = stlmm_color("secondary")),
guide = "none"
) +
scale_linewidth_manual(values = c("FALSE" = 0.25, "TRUE" = 1), guide = "none")
6 Fit
The sampler settings below use a longer run than the earlier direct-estimate articles because the SVC model has more posterior dependence. For a final analysis, increase the run length as needed and check convergence diagnostics before interpreting results.
n_samples <- 24000
burnin <- 12000
chains <- 3
n_keep <- 100
posterior_thin <- max(1L, floor((n_samples - burnin) / n_keep))
posterior_sub_sample <- list(start = burnin + 1, thin = posterior_thin)
chain_control <- list(seed = 11, dispersion = 1.5)
warmup_control <- list(batch_length = 25, min_batches = 10)
summary_parameters <- c(
"(Intercept)",
"county_mean_tcc_scaled",
"kappa",
"tau0_sq",
"car_1_sigma_sq",
"car_1_rho",
"car_time_1_sigma_sq",
"car_time_1_rho",
"car_time_1_phi"
)The n_keep value controls how many post-burn-in draws per chain are retained for recovery and fitted-value summaries. The fit summary uses all post-burn-in MCMC samples.
In the model call below, direct_biomass_model is \(\hat{y}_i\), county_mean_tcc_scaled is \(x_i\), direct_biomass_vhat_model is \(\hat{v}_i\), and n_eff_model is \(n_i\). The term county_mean_tcc_scaled:car(county_fips, graph = g, car_model = "leroux") adds the county-level spatially varying TCC coefficient.
fit <- stLMM(
direct_biomass_model ~
county_mean_tcc_scaled +
county_mean_tcc_scaled:car(county_fips, graph = g, car_model = "leroux") +
car_time(county_fips, year, graph = g, car_model = "leroux") +
resid(
model = "scaled",
variance = direct_biomass_vhat_model,
n = n_eff_model,
shrinkage = 10,
kappa_log_prior = c(mean = 0, sd = 1)
),
data = direct_estimates,
priors = list(
car_1 = list(
sigma_sq = half_t(
df = 3,
scale = 0.5 * sd(observed_direct$direct_biomass_model, na.rm = TRUE)
),
rho = uniform(0.01, 0.99)
),
car_time_1 = list(
sigma_sq = half_t(
df = 3,
scale = sd(observed_direct$direct_biomass_model, na.rm = TRUE)
),
rho = uniform(0.01, 0.99),
phi = uniform(0.01, 0.99)
)
),
n_samples = n_samples,
chains = chains,
chain_control = chain_control,
warmup = warmup_control,
verbose = TRUE,
n_report = 500
)
fit_summary <- summary(fit, burn = burnin, parameters = summary_parameters)Show fit summary code
fit_summarystLMM multi-chain summary
formula: direct_biomass_model ~ county_mean_tcc_scaled + county_mean_tcc_scaled:car(county_fips, graph = g, car_model = "leroux") + car_time(county_fips, year, graph = g, car_model = "leroux") + resid(model = "scaled", variance = direct_biomass_vhat_model, n = n_eff_model, shrinkage = 10, kappa_log_prior = c(mean = 0, sd = 1))
chains: 3
family: gaussian
observations: 390 (315 observed, 75 missing response)
posterior draws per chain: 24000 (12000 used after burn = 12000)
process terms: 2
Parameters:
mean sd q2.5 q50.0 q97.5
(Intercept) 81.4666 8.2451 67.7171 80.2517 100.1422
county_mean_tcc_scaled 65.0901 14.6247 35.9891 65.4380 91.9345
kappa 0.4545 0.0884 0.3139 0.4451 0.6564
tau0_sq 15639.4811 7053.7596 6331.7058 14279.7532 33528.8858
car_1_sigma_sq 2576.8672 1931.9337 101.9593 2200.1067 7480.5391
car_1_rho 0.3361 0.2483 0.0200 0.2735 0.9173
car_time_1_sigma_sq 608.4787 711.1273 0.0506 351.2709 2320.9478
car_time_1_rho 0.4602 0.2484 0.0457 0.4485 0.9184
car_time_1_phi 0.8263 0.2612 0.1050 0.9749 0.9897
Chain diagnostics:
parameter rhat effective_size
(Intercept) (Intercept) 1.0080 1129.0054
county_mean_tcc_scaled county_mean_tcc_scaled 1.0021 1588.5417
kappa kappa 1.0030 1153.6700
tau0_sq tau0_sq 1.0048 1170.0709
car_1_sigma_sq car_1_sigma_sq 1.0093 310.1831
car_1_rho car_1_rho 1.0022 739.2859
car_time_1_sigma_sq car_time_1_sigma_sq 1.0255 209.2757
car_time_1_rho car_time_1_rho 1.0026 1171.6224
car_time_1_phi car_time_1_phi 1.0092 367.5569The county_mean_tcc_scaled row is the fixed-effect TCC slope. Because the covariate is standardized, this coefficient is measured per one standard deviation increase in county mean TCC. The car_1 rows describe the CAR prior for spatial variation around that fixed-effect slope, and the car_time_1 rows describe the remaining county-time structure after the spatially varying TCC effect is included.
The kappa and tau0_sq rows in the summary describe how the model recalibrates the direct-estimate variances. The parameter kappa is the overall multiplier: values below one shrink the supplied variance template, and values above one inflate it. The parameter tau0_sq is the common variance scale used when a county-year has limited FIA support.
In this fit, the remaining CAR-time variance is small after adding the spatially varying TCC effect. That is a useful diagnostic: when a flexible mean model explains most of the broad county-year structure, the residual time-process variance can become weakly identified and should not be overinterpreted.
7 Recovery and Fitted Means
Structured process terms are collapsed during Gaussian model fitting. The recover() call draws both the county-year CAR-time effects and the county-level TCC slope deviations from retained posterior parameter draws.
rec <- recover(
fit,
sub_sample = posterior_sub_sample
)The recovered car_1 process gives county-specific deviations from the global TCC slope. Adding those deviations to the posterior draws of county_mean_tcc_scaled gives the full county-level TCC coefficient. The intercept is not added here because the map is a slope surface: it shows the expected change in biomass for a one standard deviation increase in county mean TCC.
Show TCC coefficient map code
rec_draws <- as_samples(rec, include_w = TRUE, metadata = FALSE)
w_svc_cols <- paste0("w_car_1_", seq_along(g$ids))
tcc_coef_draws <- sweep(
rec_draws[, w_svc_cols, drop = FALSE],
1,
rec_draws$county_mean_tcc_scaled,
`+`
)
tcc_coef_summary <- summarize_draw_matrix(tcc_coef_draws, prefix = "tcc_coef_") |>
mutate(county_fips = g$ids)
tcc_coef_map <- wa_counties |>
left_join(tcc_coef_summary, by = "county_fips")
ggplot(tcc_coef_map) +
geom_sf(aes(fill = tcc_coef_mean), color = "grey80", linewidth = 0.12) +
coord_sf(expand = FALSE) +
scale_fill_gradientn(
colors = stlmm_continuous_palette(palette = "navia"),
name = "Mg/ha per\nSD TCC"
) +
labs(title = "Posterior mean TCC coefficient")
The fitted values are posterior draws of the latent county-year mean for the same county-year support used in the fit. This support includes county-years with missing direct estimates. Those rows did not contribute to the likelihood, but their CAR-time effects can still be recovered because their county and year define latent support nodes.
Show fitted-value summary code
fitted_draws <- as.matrix(as_samples(fitted(rec, summary = FALSE), metadata = FALSE))
county_year_summary <- direct_estimates |>
bind_cols(
summarize_draw_matrix(fitted_draws, prefix = "theta_") |>
select(-prediction_row)
)The resulting summary table has the common output shape used throughout the series: one row per county-year, direct-estimate information where available, and posterior means and 95% credible intervals for the model-based county-year biomass mean.
Show selected county-year summary code
show_table(
county_year_summary |>
filter(year == 2024) |>
arrange(desc(theta_mean)) |>
select(
county, n, direct_biomass_model, direct_biomass_se_model,
theta_mean, theta_lower, theta_upper
) |>
slice_head(n = 10) |>
mutate(
across(
c(direct_biomass_model, direct_biomass_se_model, theta_mean, theta_lower, theta_upper),
~ fmt_num(.x, 1)
)
),
caption = "Highest 2024 model-smoothed county-year biomass means, with 95% posterior credible intervals."
)| county | n | direct_biomass_model | direct_biomass_se_model | theta_mean | theta_lower | theta_upper |
|---|---|---|---|---|---|---|
| Skamania | 0 | NA | NA | 269.2 | 248.1 | 289.4 |
| Clallam | 3 | 194.2 | 99.8 | 250.1 | 216.3 | 279.8 |
| Jefferson | 2 | 139.0 | 23.2 | 216.5 | 184.9 | 243.9 |
| Lewis | 3 | 80.3 | 80.3 | 215.7 | 191.8 | 237.4 |
| Skagit | 5 | 183.8 | 88.0 | 180.9 | 153.8 | 205.3 |
| Grays Harbor | 3 | 145.9 | 23.1 | 170.7 | 145.7 | 193.4 |
| Mason | 1 | NA | NA | 153.5 | 125.7 | 182.4 |
| Snohomish | 4 | 133.6 | 104.8 | 152.0 | 128.1 | 175.8 |
| King | 7 | 140.7 | 76.0 | 141.7 | 119.3 | 159.8 |
| Pierce | 4 | 283.3 | 101.3 | 137.7 | 115.1 | 159.2 |
8 Direct Estimates and Smoothed Means
The next figure compares observed direct estimates with model-smoothed county-year means. Counties with no direct estimate in a year can still receive a model-based estimate because their county-year latent effect is part of the CAR-time support.
Show direct-versus-smoothed map code
panel_years <- c(2016, 2018, 2020, 2022, 2024)
map_dat <- wa_counties |>
left_join(
county_year_summary |> filter(year %in% panel_years),
by = "county_fips"
)
map_long <- bind_rows(
map_dat |> mutate(quantity = "direct estimate", value = direct_biomass_model),
map_dat |> mutate(quantity = "model-smoothed\nmean", value = theta_mean)
) |>
mutate(quantity = factor(quantity, levels = c("direct estimate", "model-smoothed\nmean")))
ggplot(map_long) +
geom_sf(aes(fill = value), color = "grey80", linewidth = 0.12) +
coord_sf(expand = FALSE) +
facet_grid(quantity ~ year) +
scale_fill_gradientn(
colors = stlmm_palette(),
name = "Mg/ha",
na.value = "grey92"
) +
theme(
panel.spacing = grid::unit(0.02, "lines"),
strip.text = element_text(margin = margin(1, 1, 1, 1)),
plot.margin = margin(0, 0, 0, 0)
)
The scatter plot shows the same comparison only for county-years used in the direct-estimate likelihood. The model is not trying to reproduce every noisy direct estimate; it smooths them according to the scaled variance model and the county-time dependence structure.
Show direct-versus-smoothed scatter code
scatter_dat <- county_year_summary |>
filter(!is.na(direct_biomass_model))
fit_axis_limits <- range(
c(scatter_dat$direct_biomass_model, scatter_dat$theta_mean, 0),
na.rm = TRUE
)
ggplot(scatter_dat, aes(x = direct_biomass_model, y = theta_mean)) +
geom_abline(slope = 1, intercept = 0, color = "grey45", linewidth = 0.45) +
geom_point(
aes(size = 1 / direct_biomass_vhat_model, color = year),
alpha = 0.55
) +
scale_size_continuous(range = c(0.5, 3.5), guide = "none") +
scale_color_viridis_c(option = "C") +
coord_equal(xlim = fit_axis_limits, ylim = fit_axis_limits) +
labs(
x = "Direct estimate Mg/ha",
y = "CAR-time posterior mean Mg/ha",
color = "Year"
)
9 County Time Series
Time series show how the model smooths through years with sparse or missing direct estimates. The example counties below are selected to include different FIA units and biomass ranges.
Show county time-series code
selected_counties <- c("Clallam", "King", "Okanogan", "Yakima")
series_dat <- county_year_summary |>
filter(county %in% selected_counties) |>
mutate(county = factor(county, levels = selected_counties))
ggplot(series_dat, aes(x = year)) +
geom_ribbon(
aes(ymin = theta_lower, ymax = theta_upper),
fill = "#9ecae1",
alpha = 0.45
) +
geom_line(aes(y = theta_mean), color = stlmm_color("primary"), linewidth = 0.7) +
geom_point(
aes(y = direct_biomass_model, size = n),
color = stlmm_color("secondary"),
alpha = 0.75,
na.rm = TRUE
) +
facet_wrap(~ county, ncol = 2, scales = "free_y") +
scale_x_continuous(breaks = seq(min(series_dat$year), max(series_dat$year), by = 5)) +
scale_size_continuous(range = c(1, 3.5), name = "Plot n") +
labs(
x = "Year",
y = "Live aboveground biomass Mg/ha"
)
The ribbons are 95% posterior credible intervals for the latent county-year mean. Points are model-ready direct estimates, with point size proportional to the number of FIA plot rows used in that county-year direct estimate. These intervals do not include posterior predictive error for a new direct estimate; the direct-estimate observation variance is represented by the noisy points, not added to the ribbon. Comparing this figure with the earlier articles shows the latent-mean intervals narrowing as the mean model becomes more informative. That is useful, but it should not be read as saying future direct estimates would be that precise.
10 Spatially Varying TCC Effects
The global TCC model assumes the same biomass-TCC association in every county. This model relaxes that assumption by adding a county-level CAR deviation to the global slope. Neighboring counties borrow strength when estimating those slope deviations, so the coefficient map should be read as a smoothed surface rather than independent county-by-county regressions.
The practical interpretation is that the same TCC value can imply different biomass depending on where it occurs. A high TCC county on the Olympic Peninsula can correspond to more biomass than a similarly high TCC county in eastern Washington, because forest type, productivity, and stand structure differ. The SVC gives the model a way to learn that regional difference.
The scaled residual variance model remains a separate part of the hierarchy. The supplied direct-estimate variances still describe relative precision across county-years, while the SVC changes how TCC enters the latent mean.
The caveat is that TCC is itself strongly spatially patterned. In this dataset, low TCC, low biomass, and eastern counties often occur together, so the SVC can be partly confounded with broad east-west forest/nonforest structure. That makes the model useful for exploring the software and workflow, but the coefficient surface should be interpreted with diagnostic care.
WAIC can be used to compare this model with the global-TCC scaled-variance model because both use the same likelihood rows. This article leaves that comparison to a targeted model-assessment analysis so the focus stays on the modeling extension itself.
11 Summary
This article adds the first spatially varying coefficient to the area-response workflow:
- the response rows are model-ready county-year direct estimates;
- single-plot county-years remain in the county-year support but do not enter the direct-estimate likelihood;
- county mean TCC enters the latent mean model through a global coefficient and a county-level CAR deviation;
- scaled direct-estimate variances enter through
resid(model = "scaled", variance = ..., n = ...); - the county graph defines spatial borrowing for both the TCC coefficient surface and the CAR-time biomass surface;
-
car_time()adds separable county-time smoothing; -
recover()is needed before mapping the TCC coefficient surface or computing fitted values; - the common output is a county-year table of posterior means and 95% credible intervals.
This closes the first area-response sequence: fixed direct-estimate variances, scaled direct-estimate variances, a global auxiliary predictor, and finally a spatially varying auxiliary-predictor effect.