fit_gradients: Fit Gradient Models to Data

In r4pde: Companion to R for Plant Disease Epidemiology Book

Fit Gradient Models to Data

Description

This function fits three gradient models (exponential, power, and modified power) to given data. It then ranks the models based on their R-squared values and returns diagnostic plots for each model.

Usage

fit_gradients(data, C = 1)

Arguments

data	A dataframe containing the data, with columns "x" representing distances and "Y" representing the corresponding measurements or counts.
C	A constant to be used in the modified power model. Defaults to 1.

Value

A list containing:

data	The input data, which will include an additional column 'mod_x'.
results_table	A table of the model parameters and R-squared values.
plot_exponential	Diagnostic plot for the exponential model.
plot_power	Diagnostic plot for the power model.
plot_modified_power	Diagnostic plot for the modified power model.
plot_exponential_original	Plot of the original data with the exponential model fit.
plot_power_original	Plot of the original data with the power model fit.
plot_modified_power_original	Plot of the original data with the modified power model fit.

See Also

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

Examples

x <- c(0.8, 1.6, 2.4, 3.2, 4, 7.2, 12, 15.2, 21.6, 28.8)
Y <- c(184.9, 113.3, 113.3, 64.1, 25, 8, 4.3, 2.5, 1, 0.8)
grad1 <- data.frame(x = x, Y = Y)
library(ggplot2)
mg <- fit_gradients(grad1, C = 0.4)
mg$plot_power_original +
  labs(title = "", x = "Distance from focus (m)", y = "Count of lesions")

r4pde documentation built on July 2, 2025, 5:09 p.m.