Package {BayesRE}

Example Rice Yield Dataset

Description

Simulated rice yield dataset with temperature and precipitation.

Usage

bayesre_example

Format

A data frame with 130 rows and 5 variables:

Location

Location ID

Year

Year

Yield

Crop yield (t/ha)

Temp

Mean seasonal temperature (°C)

Precip

Seasonal precipitation (mm)

Compute Model Metrics

Description

Computes root mean squared error (RMSE) and root mean absolute percentage error (RMAPE) from posterior mean predictions.

Usage

compute_metrics(predictions)

Arguments

Value

A named list with two numeric elements:

RMSE

Root mean squared error between observed and posterior mean predicted yield. Expressed in the same units as the yield variable.

RMAPE

Root mean absolute percentage error, expressed as a percentage. Computed as 100 \times \sqrt{\overline{|e_i / y_i|}}, where e_i is the prediction error and y_i is the observed yield for observation i.

Examples

# Small deterministic toy predictions; runs in well under 5 seconds.
toy_predictions <- data.frame(
  Yield_obs = c(10, 12, 15),
  Yhat_mean = c(9.5, 12.5, 14),
  Yhat_lo = c(8, 11, 13),
  Yhat_hi = c(11, 14, 16)
)
metrics <- compute_metrics(toy_predictions)
stopifnot(is.list(metrics), all(c("RMSE", "RMAPE") %in% names(metrics)))

# A full Stan workflow is intentionally excluded from the automatic
# example run because model compilation/sampling can exceed 5 seconds.

data(bayesre_example)
fit <- fit_bayesre(bayesre_example, chains = 1, iter = 500, warmup = 200)
pred <- predict_bayesre(fit, bayesre_example)
compute_metrics(pred)

Fit Bayesian Regression Model

Description

Fits Bayesian linear regression using Stan.

Usage

fit_bayesre(data, chains = 1, iter = 500, warmup = 200)

Arguments

Value

A stanfit object containing the posterior samples. Use predict_bayesre to obtain predictions, plot_diagnostics for MCMC trace plots, and plot_response_surface for a yield response heatmap.

Plot Diagnostics

Description

Generates MCMC trace plots for the model parameters ...

Usage

plot_diagnostics(fit)

Arguments

Value

A ggplot object (the trace-plot grid) returned invisibly by bayesplot::mcmc_trace(). The plot is also printed to the active graphics device as a side effect.

Plot Observed vs Predicted Yield

Description

Generates an observed vs posterior mean predicted yield plot with 95

Usage

plot_observed_vs_predicted(
  predictions,
  save = FALSE,
  filename = NULL,
  width = 8,
  height = 6,
  dpi = 300
)

Arguments

Value

A ggplot object.

Examples

# Small toy predictions; no Stan model is needed.
toy_predictions <- data.frame(
  Yield_obs = c(10, 12, 15, 17),
  Yhat_mean = c(10.2, 11.7, 14.6, 17.4),
  Yhat_lo = c(9, 10.5, 13.5, 16),
  Yhat_hi = c(11.5, 13, 15.8, 18.5)
)
p <- plot_observed_vs_predicted(toy_predictions)
stopifnot(inherits(p, "ggplot"))
plot_observed_vs_predicted(
  toy_predictions,
  save = TRUE,
  filename = tempfile(fileext = ".png")
)

# A full Stan workflow is intentionally excluded from the automatic
# example run because model compilation/sampling can exceed 5 seconds.

data(bayesre_example)
fit <- fit_bayesre(bayesre_example, chains = 1, iter = 500, warmup = 200)
pred <- predict_bayesre(fit, bayesre_example)
plot_observed_vs_predicted(pred)

Plot Yield Response Heatmap

Description

Generates a posterior mean yield response surface over a grid of temperature and precipitation values, using the posterior means of the intercept and regression coefficients extracted from the fitted model.

Usage

plot_response_surface(
  fit,
  temp_range = c(20, 35),
  precip_range = c(50, 200),
  grid_size = 50
)

Arguments

Value

A ggplot object displaying a tile heatmap with:

• Temperature (degrees Celsius) on the x-axis.

• Precipitation (mm) on the y-axis.

• Posterior mean predicted yield mapped to the fill colour using the viridis colour scale.

The predicted yield at each grid point is computed as \hat{\alpha} + \hat{\beta}_{\text{temp}} \times T + \hat{\beta}_{\text{precip}} \times P, where \hat{\alpha}, \hat{\beta}_{\text{temp}}, and \hat{\beta}_{\text{precip}} are the posterior means of the respective parameters.

Plot Response Surface with Observations

Description

Overlays observed temperature-precipitation data points on the posterior mean yield response surface produced by plot_response_surface.

Usage

plot_response_surface_data(fit, data)

Arguments

Value

A ggplot object: the posterior mean yield heatmap from plot_response_surface with the observed temperature-precipitation points from data overlaid as filled black circles (size 1.5, alpha 0.7). The temperature and precipitation ranges of the heatmap default to c(20, 35) and c(50, 200) respectively; pass a customised base plot via plot_response_surface if different ranges are needed.

Posterior Predictions

Description

Draws posterior predictions from a fitted stanfit object and summarises them per observation.

Usage

predict_bayesre(fit, data)

Arguments

Value

A data frame with one row per observation and four columns:

Yield_obs

Observed yield values from data$Yield.

Yhat_mean

Posterior mean of the predicted yield.

Yhat_lo

2.5th percentile of the posterior predictive distribution (lower bound of the 95% credible interval).

Yhat_hi

97.5th percentile of the posterior predictive distribution (upper bound of the 95% credible interval).