Package {leafareaR}

leafareaR: Leaf Area Modeling, Evaluation, and Prediction

Description

The leafareaR package provides tools for leaf area estimation based on leaf length, leaf width, and observed leaf area data. It includes routines for input validation, generation of derived predictors, descriptive statistics, exploratory graphics, matrix plots, linear models, nonlinear models, mixed models, model evaluation, ranking, prediction, and a built-in Shiny application.

Author(s)

Maintainer: Joao Everthon da Silva Ribeiro everthonribeiro10@gmail.com

Authors:

• Ester dos Santos Coelho

• Toshik Iarley da Silva

See Also

Useful links:

• https://github.com/agrobioestat/leafareaR

• Report bugs at https://github.com/agrobioestat/leafareaR/issues

Calculate absolute bias

Description

Calculate absolute bias

Usage

la_abs_bias_metric(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_abs_bias_metric(obs, pred)

Add equation information to a results table

Description

Adds equation, coefficients_text, and random_effect columns to the results or summary table returned by a model-fitting function.

Usage

la_add_equation_to_results(fit_object, digits = 4)

Arguments

Value

A data.frame.

Calculate prediction bias

Description

Calculate prediction bias

Usage

la_bias(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_bias(obs, pred)

Build a readable equation from a fitted model

Description

Returns a readable equation starting with ⁠LA =⁠.

Usage

la_build_equation(model, digits = 4)

Arguments

Value

A character string.

Calculate Lin's concordance correlation coefficient

Description

Calculate Lin's concordance correlation coefficient

Usage

la_ccc(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_ccc(obs, pred)

Create derived leaf parameters

Description

Generates derived variables from leaf length (L) and leaf width (W) to support leaf area (LA) modeling.

Usage

la_create_derived(data, variables = "all", keep_original = TRUE)

Arguments

Value

A data.frame with derived variables added.

Examples

data(leafarea_sample)
head(la_create_derived(leafarea_sample, variables = c("LW", "L2", "W2")))

Calculate Willmott's index of agreement

Description

Calculate Willmott's index of agreement

Usage

la_d(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_d(obs, pred)

Summarize the default leaf area variables

Description

Returns descriptive statistics for the default variables available in a leafareaR dataset, using any combination of L, W, and LA found in the supplied data.frame.

Usage

la_descriptive_default(data, na.rm = TRUE, digits = 4)

Arguments

Value

A data.frame with descriptive statistics for the available default variables.

Examples

data(leafarea_sample)
la_descriptive_default(leafarea_sample)

Calculate descriptive statistics for selected variables

Description

Computes a descriptive summary for selected numeric variables in a leaf area dataset. The output is intended to support data exploration before model fitting and can be applied to the original measurements as well as derived predictors.

Usage

la_descriptive_stats(data, variables = NULL, na.rm = TRUE, digits = 4)

Arguments

Value

A data.frame with descriptive statistics for each selected variable.

Examples

data(leafarea_sample)
la_descriptive_stats(leafarea_sample, variables = c("L", "W", "LA"))

Evaluate all linear models from a la_fit_linear_models() object

Description

Evaluate all linear models from a la_fit_linear_models() object

Usage

la_evaluate_linear_models(fit_object, digits = 4)

Arguments

Value

A data.frame with metrics for all candidate linear models.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
met[, c("model_id", "RMSE", "R2")]

Evaluate all mixed models from a la_fit_mixed_models() object

Description

Evaluate all mixed models from a la_fit_mixed_models() object

Usage

la_evaluate_mixed_models(fit_object, digits = 4)

Arguments

Value

A data.frame with metrics for all candidate mixed models.

Examples

data(leafarea_sample)
fit <- la_fit_mixed_models(leafarea_sample, group_var = "species")
la_evaluate_mixed_models(fit)[, c("model_id", "RMSE", "R2")]

Evaluate a single fitted model

Description

Evaluate a single fitted model

Usage

la_evaluate_model(
  model,
  data = NULL,
  response = "LA",
  model_id = NA_character_,
  model_type = NA_character_,
  digits = 4
)

Arguments

Value

A one-row data.frame.

Examples

data(leafarea_sample)
dat <- la_create_derived(leafarea_sample, variables = "LW")
m <- lm(LA ~ LW, data = dat)
la_evaluate_model(m, model_id = "lm_LW", model_type = "linear")

Evaluate all nonlinear models from a la_fit_nonlinear_models() object

Description

Evaluate all nonlinear models from a la_fit_nonlinear_models() object

Usage

la_evaluate_nonlinear_models(fit_object, digits = 4)

Arguments

Value

A data.frame with metrics for all candidate nonlinear models.

Examples

data(leafarea_sample)
fit <- la_fit_nonlinear_models(leafarea_sample, 
                               models = c("power_LW", "exponential_LW"))
la_evaluate_nonlinear_models(fit)[, c("model_id", "RMSE", "R2")]

Extract model coefficients

Description

Returns a coefficient table from a fitted linear, nonlinear, or mixed model.

Usage

la_extract_coefficients(model)

Arguments

Value

A data.frame with coefficient names and estimates.

Display labels for leaf variables

Description

Returns user-friendly labels as either a named character vector or a plain character vector.

Usage

la_feature_display_names(named = FALSE)

Arguments

Value

A character vector.

Display labels for leaf variables

Description

Returns a table of internal variable names and user-friendly labels used throughout the package interface.

Usage

la_feature_labels()

Value

A data.frame with internal names and display labels.

Fit candidate linear models for leaf area estimation

Description

Fits a default or user-supplied set of linear models using L, W, LA, and derived variables created automatically when needed.

Usage

la_fit_linear_models(
  data,
  formulas = NULL,
  include_no_intercept = TRUE,
  include_multiple = TRUE,
  include_polynomial = TRUE
)

Arguments

Value

A list with fitted models, formulas, modeling data, and a summary table enriched with equation information.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
names(fit$models)

Fit candidate linear mixed-effects models for leaf area estimation

Description

Fits a default or user-supplied set of linear mixed-effects models using L, W, LA, derived variables created automatically when needed, and a user-supplied grouping variable.

Usage

la_fit_mixed_models(
  data,
  group_var,
  formulas = NULL,
  random_slope = FALSE,
  include_multiple = TRUE,
  include_polynomial = TRUE,
  REML = FALSE,
  control = NULL
)

Arguments

Value

A list with fitted models, formulas, modeling data, and a summary table enriched with equation information.

Examples

data(leafarea_sample)
fit <- la_fit_mixed_models(leafarea_sample, group_var = "species")
names(fit$models)

Fit multiple nonlinear models

Description

Fits built-in or user-supplied nonlinear model specifications.

Usage

la_fit_nonlinear_models(
  data,
  models = NULL,
  specs = NULL,
  control = stats::nls.control(maxiter = 200, warnOnly = TRUE)
)

Arguments

Value

A list containing fitted models, specifications, data, and summary.

Examples

data(leafarea_sample)
fit <- la_fit_nonlinear_models(leafarea_sample, models = c("power_LW", "exponential_LW"))
names(fit$models)
fit$summary[, c("model_id", "converged")]

Summarize a validated leaf area dataset

Description

Produces a concise overview of a validated dataset, including its dimensions, variable names, and summary statistics for L, W, and LA. This function is useful as a quick check before creating derived variables or fitting candidate models.

Usage

la_input_overview(data)

Arguments

Value

A list with the number of rows and columns, variable names, and a summary of the main measurement variables.

Examples

data(leafarea_sample)
validated_data <- la_validate_input(leafarea_sample)
overview <- la_input_overview(validated_data)
overview$n_rows
overview$summary

Extract fitted values from linear model results

Description

Extracts observed and fitted values for one selected linear model from the object returned by la_fit_linear_models().

Usage

la_linear_fitted_values(fit_object, model_id)

Arguments

Value

A data.frame with observed, fitted, and residual values.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
head(la_linear_fitted_values(fit, "lm_LW"))

List default linear model formulas

Description

Returns the default set of candidate linear formulas used by la_fit_linear_models().

Usage

la_linear_formulas(
  include_no_intercept = TRUE,
  include_multiple = TRUE,
  include_polynomial = TRUE
)

Arguments

Value

A named list of formulas.

Examples

names(la_linear_formulas())

List available derived variables

Description

Returns the default derived variables available in leafareaR.

Usage

la_list_derived()

Value

A character vector.

Examples

la_list_derived()

Calculate mean absolute error

Description

Calculate mean absolute error

Usage

la_mae(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_mae(obs, pred)

Calculate mean absolute percentage error

Description

Calculate mean absolute percentage error

Usage

la_mape(observed, predicted)

Arguments

Value

A numeric value in percentage.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_mape(obs, pred)

Create a matrix plot for selected variables

Description

Generates a scatterplot matrix for selected numeric variables.

Usage

la_matrixplot(
  data,
  variables = NULL,
  hist = TRUE,
  pch = 19,
  cex = 0.6,
  col = "darkgreen",
  main = NULL
)

Arguments

Value

Invisibly returns the selected data used in the matrix plot.

Examples

data(leafarea_sample)

if(interactive()){
  la_matrixplot(leafarea_sample, variables = c("L", "W", "LA"))
}

Create a default matrix plot for leaf variables

Description

Generates a matrix plot using available default variables among L, W, and LA.

Usage

la_matrixplot_default(
  data,
  hist = TRUE,
  pch = 19,
  cex = 0.6,
  col = "darkgreen",
  main = "Matrix plot of leaf variables"
)

Arguments

Value

Invisibly returns the selected data used in the matrix plot.

Examples

data(leafarea_sample)

if(interactive()){
  la_matrixplot_default(leafarea_sample)
}

Calculate a standard metric table from observed and predicted values

Description

Calculate a standard metric table from observed and predicted values

Usage

la_metric_table(
  observed,
  predicted,
  n_parameters = NA_integer_,
  model_object = NULL,
  digits = 4
)

Arguments

Value

A one-row data.frame.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_metric_table(obs, pred)

Extract coefficients from a mixed model

Description

Extract coefficients from a mixed model

Usage

la_mixed_coefficients(fit_object, model_id)

Arguments

Value

A data.frame with fixed-effect coefficients.

Examples

data(leafarea_sample)
fit <- la_fit_mixed_models(leafarea_sample, group_var = "species")
la_mixed_coefficients(fit, names(fit$models)[1])

Extract fitted values from mixed-model results

Description

Extract fitted values from mixed-model results

Usage

la_mixed_fitted_values(fit_object, model_id)

Arguments

Value

A data.frame with observed, fitted, residual, and group values.

Examples

data(leafarea_sample)
fit <- la_fit_mixed_models(leafarea_sample, group_var = "species")
head(la_mixed_fitted_values(fit, names(fit$models)[1]))

List default mixed-model formulas

Description

Returns the default set of candidate mixed-model formulas used by la_fit_mixed_models().

Usage

la_mixed_formulas(
  group_var,
  random_slope = FALSE,
  include_multiple = TRUE,
  include_polynomial = TRUE
)

Arguments

Value

A named list of formulas.

Examples

names(la_mixed_formulas("species"))

Calculate mean squared error

Description

Calculate mean squared error

Usage

la_mse(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_mse(obs, pred)

Return coefficients from a selected nonlinear model

Description

Return coefficients from a selected nonlinear model

Usage

la_nonlinear_coefficients(fit_object, model_id)

Arguments

Value

A data.frame with parameter estimates.

Examples

data(leafarea_sample)
fit <- la_fit_nonlinear_models(leafarea_sample, models = c("power_LW"))
la_nonlinear_coefficients(fit, "power_LW")

Extract observed, fitted values and residuals for a selected nonlinear model

Description

Extract observed, fitted values and residuals for a selected nonlinear model

Usage

la_nonlinear_fitted_values(fit_object, model_id)

Arguments

Value

A data.frame with observed, fitted, and residual values.

Examples

data(leafarea_sample)
fit <- la_fit_nonlinear_models(leafarea_sample, models = c("power_LW"))
head(la_nonlinear_fitted_values(fit, "power_LW"))

Default nonlinear model specifications

Description

Returns the default built-in nonlinear candidate specifications.

Usage

la_nonlinear_specs()

Value

A named list of nonlinear model specifications.

Examples

names(la_nonlinear_specs())

Calculate Nash-Sutcliffe efficiency

Description

Calculate Nash-Sutcliffe efficiency

Usage

la_nse(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_nse(obs, pred)

Observed versus predicted leaf area plot

Description

Creates a plot comparing observed and predicted leaf area values.

Usage

la_plot_observed_predicted(
  observed,
  predicted,
  model_name = "Selected model",
  point_size = 2.2,
  alpha = 0.75
)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  fit <- la_fit_linear_models(leafarea_sample)
  vals <- la_linear_fitted_values(fit, model_id = "lm_LW")
  p <- la_plot_observed_predicted(vals$observed, vals$fitted, model_name = "lm_LW")
  print(p)
}

Histogram of residuals

Description

Creates a histogram of residuals from observed and predicted values.

Usage

la_plot_residual_histogram(
  observed,
  predicted,
  bins = 30,
  model_name = "Selected model"
)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  fit <- la_fit_linear_models(leafarea_sample)
  vals <- la_linear_fitted_values(fit, model_id = "lm_LW")
  p <- la_plot_residual_histogram(vals$observed, vals$fitted, model_name = "lm_LW")
  print(p)
}

QQ plot of residuals

Description

Creates a QQ plot of residuals from observed and predicted values.

Usage

la_plot_residual_qq(observed, predicted, model_name = "Selected model")

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  fit <- la_fit_linear_models(leafarea_sample)
  vals <- la_linear_fitted_values(fit, model_id = "lm_LW")
  p <- la_plot_residual_qq(vals$observed, vals$fitted, model_name = "lm_LW")
  print(p)
}

Residuals versus fitted values plot

Description

Creates a residual diagnostic plot from observed and predicted values.

Usage

la_plot_residuals(
  observed,
  predicted,
  model_name = "Selected model",
  point_size = 2.2,
  alpha = 0.75
)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  fit <- la_fit_linear_models(leafarea_sample)
  vals <- la_linear_fitted_values(fit, model_id = "lm_LW")
  p <- la_plot_residuals(vals$observed, vals$fitted, model_name = "lm_LW")
  print(p)
}

Scatter plot between two selected variables

Description

Creates a scatter plot for two selected variables in a leaf area dataset.

Usage

la_plot_scatter(
  data,
  x,
  y = "LA",
  color_var = NULL,
  add_smooth = TRUE,
  point_size = 2.2,
  alpha = 0.75
)

Arguments

Value

A ggplot object.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  p <- la_plot_scatter(leafarea_sample, x = "L", y = "LA")
  print(p)
}

Scatter plots for multiple selected predictors against leaf area

Description

Creates a list of scatter plots using the selected predictors against the response variable.

Usage

la_plot_scatter_set(
  data,
  predictors = c("L", "W", "LW"),
  response = "LA",
  add_smooth = TRUE,
  point_size = 2.2,
  alpha = 0.75
)

Arguments

Value

A named list of ggplot objects.

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  data(leafarea_sample)
  dat <- la_create_derived(leafarea_sample, variables = c("LW"))
  plots <- la_plot_scatter_set(dat, predictors = c("L", "W", "LW"))
  print(plots[[1]])
}

Predict using one selected model from a fit object

Description

Predict using one selected model from a fit object

Usage

la_predict_from_results(
  fit_object,
  model_id = 1,
  newdata,
  allow_new_levels = TRUE,
  re_form = NULL
)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA")]
la_predict_from_results(fit, model_id = "lm_LW", newdata = newdata_ex)

Predict from a linear model

Description

Predict from a linear model

Usage

la_predict_linear_model(model, newdata)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA")]
la_predict_linear_model(fit$models[["lm_LW"]], newdata_ex)

Predict from a mixed model

Description

Predict from a mixed model

Usage

la_predict_mixed_model(model, newdata, allow_new_levels = TRUE, re_form = NULL)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_mixed_models(leafarea_sample, group_var = "species")
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA", "species")]
la_predict_mixed_model(fit$models[[names(fit$models)[1]]], newdata_ex)

Predict from a fitted model

Description

Generic dispatcher for prediction from linear, nonlinear, or mixed models.

Usage

la_predict_model(
  model,
  newdata,
  model_type = c("auto", "linear", "nonlinear", "mixed"),
  allow_new_levels = TRUE,
  re_form = NULL
)

Arguments

Value

A data.frame containing the prediction columns.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA")]
la_predict_model(fit$models[["lm_LW"]], newdata_ex)

Predict from a nonlinear model

Description

Predict from a nonlinear model

Usage

la_predict_nonlinear_model(model, newdata)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_nonlinear_models(leafarea_sample, models = c("power_LW"))
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA")]
la_predict_nonlinear_model(fit$models[["power_LW"]], newdata_ex)

Predict from the top-ranked model

Description

Predict from the top-ranked model

Usage

la_predict_top_ranked(
  ranked_table,
  fit_object,
  rank_position = 1,
  newdata,
  allow_new_levels = TRUE,
  re_form = NULL
)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
ranked <- la_rank_models(met)
newdata_ex <- leafarea_sample[1:5, c("L", "W", "LA")]
la_predict_top_ranked(
  ranked,
  fit,
  rank_position = 1,
  newdata = newdata_ex
)

Calculate Pearson correlation coefficient

Description

Calculate Pearson correlation coefficient

Usage

la_r(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_r(obs, pred)

Calculate coefficient of determination

Description

Calculate coefficient of determination

Usage

la_r_squared(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_r_squared(obs, pred)

Rank models using a simple metric priority rule

Description

Orders models according to a sequence of evaluation criteria.

Usage

la_rank_models(
  metrics_table,
  sort_by = c("RMSE", "MAE", "CCC", "R2", "ABS_BIAS"),
  ascending = c(TRUE, TRUE, FALSE, FALSE, TRUE)
)

Arguments

Value

A ranked data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
la_rank_models(met)

Rank models by average metric positions

Description

Computes metric-wise ranks and aggregates them using the mean rank.

Usage

la_rank_models_by_metrics(metrics_table)

Arguments

Value

A ranked data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
la_rank_models_by_metrics(met)

Rank models using a weighted score

Description

Computes a weighted score from selected metrics after min-max scaling.

Usage

la_rank_models_weighted(
  metrics_table,
  weights = list(RMSE = 0.3, MAE = 0.2, CCC = 0.2, R2 = 0.15, ABS_BIAS = 0.1, d = 0.05)
)

Arguments

Value

A ranked data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
la_rank_models_weighted(met)

Calculate root mean squared error

Description

Calculate root mean squared error

Usage

la_rmse(observed, predicted)

Arguments

Value

A numeric value.

Examples

obs <- c(10, 12, 15, 18)
pred <- c(9.8, 12.1, 14.7, 18.4)
la_rmse(obs, pred)

Select the top models from a ranking table

Description

Returns the first n rows from a ranked table.

Usage

la_top_models(
  ranking_table,
  n = 5,
  rank_column = c("rank_simple", "rank_weighted", "rank_mean")
)

Arguments

Value

A data.frame.

Examples

data(leafarea_sample)
fit <- la_fit_linear_models(leafarea_sample)
met <- la_evaluate_linear_models(fit)
ranked <- la_rank_models(met)
la_top_models(ranked, n = 3)

Validate and standardize input data for leaf area analysis

Description

Checks that the selected leaf length, leaf width, and observed leaf area columns are present and numeric, applies optional cleaning rules, and standardizes their names to L, W, and LA for downstream use in leafareaR.

Usage

la_validate_input(
  data,
  l_col = "L",
  w_col = "W",
  la_col = "LA",
  remove_na = TRUE,
  remove_nonpositive = TRUE,
  standardize_names = TRUE,
  keep_all_columns = FALSE
)

Arguments

Value

A validated data.frame ready for descriptive analysis, model fitting, prediction, and visualization in the leafareaR workflow.

Examples

data(leafarea_sample)
validated_data <- la_validate_input(leafarea_sample)
head(validated_data)

validated_with_groups <- la_validate_input(
  data = leafarea_sample,
  keep_all_columns = TRUE
)
head(validated_with_groups)

Example dataset for leaf area modeling

Description

A sample dataset included in leafareaR for testing data validation, descriptive statistics, derived variables, plotting, linear models, nonlinear models, mixed models, ranking, and prediction.

Usage

data(leafarea_sample)

Format

A data.frame with 9999 rows and 6 variables:

L

Leaf length.

W

Leaf width.

LA

Observed leaf area.

species

Species identifier.

block

Block identifier.

genotype

Genotype identifier.

Examples

data(leafarea_sample)
head(leafarea_sample)

Launch the built-in Shiny application

Description

Opens the interactive leafareaR Shiny app, which provides a graphical interface for loading example data or uploading user data, exploratory analysis, model fitting, evaluation, ranking, and prediction.

Usage

run_leafareaR_app(...)

Arguments

Value

Launches the application.

Examples

app_dir <- system.file("shiny", "leafareaR-app", package = "leafareaR")
dir.exists(app_dir)
if (interactive()) {
  run_leafareaR_app()
}