Overview

leafareaR provides a workflow for leaf area analysis based on leaf length (L), leaf width (W), and observed leaf area (LA). The package includes tools for input validation, predictor generation, model fitting, model evaluation and ranking, equation extraction, prediction, graphics, and an interactive Shiny application.

A typical analysis in leafareaR follows six steps:

1. validate the input data;
2. generate derived predictors;
3. fit candidate models;
4. evaluate and rank the fitted models;
5. inspect the selected equation;
6. generate predictions and graphics.

Example dataset

The package includes a sample dataset named leafarea_sample. The example below shows only the core variables used in most analyses.

kable(leafarea_sample[1:6, c("L", "W", "LA")], digits = 3)

Additional grouping variables such as block, species, and genotype may also be present and can be used in mixed-model workflows.

Data preparation

The first step is to validate the required variables and, when needed, create derived predictors from L and W.

dat <- la_validate_input(leafarea_sample)

dat2 <- la_create_derived(
  dat,
  variables = c("LW", "L2", "W2", "L3", "W3", "L_plus_W")
)

A compact descriptive summary of selected variables is shown below.

kable(desc_out, digits = 3)

A linear modeling workflow

Linear candidate models can be fitted directly from the prepared data and then ranked according to the selected evaluation criteria.

fit_linear <- la_fit_linear_models(dat2)
met_linear <- la_evaluate_linear_models(fit_linear)
ranked_linear <- la_rank_models(met_linear)

For illustration, the next table shows only the three best-ranked linear models.

kable(top_linear_out, digits = 4)

In this example, the top-ranked model is lm_full_poly3.

Equation extraction

A readable equation can be obtained from the selected model.

cat(best_equation)

LA = -0.1013 - 0.3075 * L + 0.8508 * W + 0.4653 * L × W + 0.0939 * L² - 0.0853 * W² - 0.0042 * L³ + 0.0142 * W³

This equation can be reported directly or used as the basis for prediction.

Prediction

Predictions can be generated from the top-ranked model for a new set of observations.

pred <- la_predict_top_ranked(
  ranked_table = ranked_linear,
  fit_object = fit_linear,
  rank_position = 1,
  newdata = dat2[1:10, ]
)

A small subset of the prediction output is shown below.

kable(pred_out, digits = 4)

Graphics

The package also includes simple graphics for exploratory analysis and model inspection.

la_plot_scatter(dat2, x = "LW", y = "LA")

la_plot_observed_predicted(
  observed = vals$observed,
  predicted = vals$fitted,
  model_name = best_id
)

Extensions to nonlinear and mixed models

The same general strategy can be extended to nonlinear and mixed models: fit, evaluate, rank, inspect the selected equation, and predict.

fit_nonlinear <- la_fit_nonlinear_models(dat2, models = c("power_LW"))
met_nonlinear <- la_evaluate_nonlinear_models(fit_nonlinear)
ranked_nonlinear <- la_rank_models(met_nonlinear)

fit_mixed <- la_fit_mixed_models(dat2, group_var = "species")
met_mixed <- la_evaluate_mixed_models(fit_mixed)
ranked_mixed <- la_rank_models(met_mixed)

Interactive use

leafareaR also provides a Shiny application for interactive analysis.

run_leafareaR_app()

The application can be used to load the built-in example dataset (leafarea_sample) or upload your own data, then select predictors, fit and compare candidate models, inspect equations, and export results and graphics.

Summary

leafareaR provides a reproducible workflow for leaf area analysis, from data preparation to model comparison, equation reporting, prediction, and visualization. The package can be used both in script-based analyses and through its interactive Shiny interface.