R: Leaf Area Modeling, Evaluation, and Prediction

# Leaf Area Modeling, Evaluation, and Prediction

## Documentation for package ‘leafareaR’ version 0.0.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

la_abs_bias_metric | Calculate absolute bias
la_add_equation_to_results | Add equation information to a results table
la_bias | Calculate prediction bias
la_build_equation | Build a readable equation from a fitted model
la_ccc | Calculate Lin's concordance correlation coefficient
la_create_derived | Create derived leaf parameters
la_d | Calculate Willmott's index of agreement
la_descriptive_default | Summarize the default leaf area variables
la_descriptive_stats | Calculate descriptive statistics for selected variables
la_evaluate_linear_models | Evaluate all linear models from a 'la_fit_linear_models()' object
la_evaluate_mixed_models | Evaluate all mixed models from a 'la_fit_mixed_models()' object
la_evaluate_model | Evaluate a single fitted model
la_evaluate_nonlinear_models | Evaluate all nonlinear models from a 'la_fit_nonlinear_models()' object
la_extract_coefficients | Extract model coefficients
la_feature_display_names | Display labels for leaf variables
la_feature_labels | Display labels for leaf variables
la_fit_linear_models | Fit candidate linear models for leaf area estimation
la_fit_mixed_models | Fit candidate linear mixed-effects models for leaf area estimation
la_fit_nonlinear_models | Fit multiple nonlinear models
la_input_overview | Summarize a validated leaf area dataset
la_linear_fitted_values | Extract fitted values from linear model results
la_linear_formulas | List default linear model formulas
la_list_derived | List available derived variables
la_mae | Calculate mean absolute error
la_mape | Calculate mean absolute percentage error
la_matrixplot | Create a matrix plot for selected variables
la_matrixplot_default | Create a default matrix plot for leaf variables
la_metric_table | Calculate a standard metric table from observed and predicted values
la_mixed_coefficients | Extract coefficients from a mixed model
la_mixed_fitted_values | Extract fitted values from mixed-model results
la_mixed_formulas | List default mixed-model formulas
la_mse | Calculate mean squared error
la_nonlinear_coefficients | Return coefficients from a selected nonlinear model
la_nonlinear_fitted_values | Extract observed, fitted values and residuals for a selected nonlinear model
la_nonlinear_specs | Default nonlinear model specifications
la_nse | Calculate Nash-Sutcliffe efficiency
la_plot_observed_predicted | Observed versus predicted leaf area plot
la_plot_residuals | Residuals versus fitted values plot
la_plot_residual_histogram | Histogram of residuals
la_plot_residual_qq | QQ plot of residuals
la_plot_scatter | Scatter plot between two selected variables
la_plot_scatter_set | Scatter plots for multiple selected predictors against leaf area
la_predict_from_results | Predict using one selected model from a fit object
la_predict_linear_model | Predict from a linear model
la_predict_mixed_model | Predict from a mixed model
la_predict_model | Predict from a fitted model
la_predict_nonlinear_model | Predict from a nonlinear model
la_predict_top_ranked | Predict from the top-ranked model
la_r | Calculate Pearson correlation coefficient
la_rank_models | Rank models using a simple metric priority rule
la_rank_models_by_metrics | Rank models by average metric positions
la_rank_models_weighted | Rank models using a weighted score
la_rmse | Calculate root mean squared error
la_r_squared | Calculate coefficient of determination
la_top_models | Select the top models from a ranking table
la_validate_input | Validate and standardize input data for leaf area analysis
leafarea_sample | Example dataset for leaf area modeling
run_leafareaR_app | Launch the built-in Shiny application