Solving Nonlinear Models with R: A Step-by-Step Guide Using ggplot2

You can follow these steps to solve the problem:

Split the data set by code:

ss <- split(dd, dd$code)

Fit a nonlinear model using nls() with the SSasymp function:

mm <- lapply(ss,
             nls,
             formula = SGP ~ SSasymp(time,a,b,c))

Note: The SSasymp function is used here, which fits the model Asym + (R0 - Asym) * exp(-exp(lrc) * input).

Calculate predictions for each chunk:

pp <- lapply(mm, predict)

Add the predictions to the original data set:

dd$pred <- unlist(pp)

Plot the data using ggplot2:

library(ggplot2); theme_set(theme_bw())
ggplot(dd, aes(x=time, y = SGP, group = code)) + 
  geom_point() +
  geom_line(aes(y = pred), colour = "blue", alpha = 0.5)

This will create a plot with the predicted values superimposed on the original data.

Here is the complete code:

# Load libraries
library(ggplot2)

# Split data set by code
ss <- split(dd, dd$code)

# Fit nonlinear model using nls() with SSasymp function
mm <- lapply(ss,
             nls,
             formula = SGP ~ SSasymp(time,a,b,c))

# Calculate predictions for each chunk
pp <- lapply(mm, predict)

# Add predictions to original data set
dd$pred <- unlist(pp)

# Plot data using ggplot2
library(ggplot2); theme_set(theme_bw())
ggplot(dd, aes(x=time, y = SGP, group = code)) + 
  geom_point() +
  geom_line(aes(y = pred), colour = "blue", alpha = 0.5)

Note: Make sure to adjust the model formula and parameters according to your specific data set.

Last modified on 2024-12-09