Leaf Area Modeling, Evaluation, and Prediction

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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