BPL: Binary Power Law Analysis for Spatial Disease Patterns

In r4pde: Tools for Quantitative Plant Disease Epidemiology

Binary Power Law Analysis for Spatial Disease Patterns

Description

This function calculates the Binary Power Law (BPL) parameters for spatial disease patterns, fits a linear model, and performs a hypothesis test for the slope.

Usage

BPL(data)

Arguments

data	A data frame containing the following columns: field: The field identifier. n: The number of observations in each quadrat. i: The incidence count in each quadrat.

Details

The function performs the following steps:

1. Summarizes the data by field to calculate the total number of observations (n_total), mean incidence (incidence_mean), observed variance (V), and binomial variance (Vbin).

2. Log-transforms the variances.

3. Fits a linear model to the log-transformed variances.

4. Tests the hypothesis that the slope of the linear model is equal to 1.

Value

A list containing the following elements:

• summary: A data frame summarizing the input data by field, including total observations (n_total), mean incidence (incidence_mean), observed variance (V), and binomial variance (Vbin).

• model_summary: A summary of the linear model fitted to the log-transformed variances.

• hypothesis_test: The result of the hypothesis test for the slope being equal to 1.

• ln_Ap: The intercept of the linear model, representing the natural logarithm of the parameter \( A_p \).

• slope: The slope of the linear model.

See Also

Other Spatial analysis: AFSD(), count_subareas(), count_subareas_random(), fit_gradients(), join_count(), oruns_test(), oruns_test_boustrophedon(), oruns_test_byrowcol(), plot_AFSD()

Examples

# Example usage with a sample data frame
result <- BPL(FHBWheat)
print(result$summary)
print(result$model_summary)
print(result$hypothesis_test)
print(paste("ln(Ap):", result$ln_Ap))
print(paste("Slope (b):", result$slope))

r4pde documentation built on Sept. 8, 2026, 1:10 a.m.