Automating Loess Predictions for Multiple Groups of Data Using R's Plyr and Nlme Packages

Loess Prediction for Many Groups of Data

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In this article, we will explore how to use the loess function in R to predict values for a continuous outcome variable (vi) based on a predictor variable (julian). We will also discuss ways to automate the process of creating predictions for multiple groups of data.

Introduction

The loess function is a non-linear regression model that can be used to fit curves through a set of data points. It is particularly useful when there are many predictors and few observations, as it can capture complex relationships between variables.

In this article, we will use the loess function to predict values for a continuous outcome variable (vi) based on a predictor variable (julian). We will also explore ways to automate the process of creating predictions for multiple groups of data using the dlply and nlsList functions from the plyr and nls packages, respectively.

Automating Loess Predictions with dlply

One way to automate the process of creating predictions for multiple groups of data is to use the dlply function from the plyr package. This function allows us to split a dataset into groups based on one or more variables, apply a function to each group, and then combine the results.

Here’s an example of how to use dlply to create predictions for multiple groups of data:

# Load required libraries
library(plyr)
library(loess)

# Create a sample dataset
set.seed(123)
vi.sample <- data.frame(
  year = sample(c(2012, 2013, 2014), size = 100, replace = TRUE),
  julian = runif(100, min = 1, max = 365),
  site = sample(c("east", "west"), size = 100, replace = TRUE),
  type = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  trt = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  plot = sample(1:10, size = 100, replace = TRUE),
  vi = rnorm(100, mean = c(0.41, 0.44, 0.52, 0.64, 0.66, 0.67, 0.64, 0.66, 0.61, 0.7), sd = 0.1)
)

# Create a list of loess fits
fits <- dlply(vi.sample, .(year, site, type, trt, plot),
              function(samp) {
                loess(vi ~ julian, model=T, na.action=na.exclude, data=samp)
              })

# Print the loess fits
print(fits)

Automating Loess Predictions with nlsList

Another way to automate the process of creating predictions for multiple groups of data is to use the nlsList function from the nls package. This function allows us to create a list of non-linear regression models, where each model corresponds to a specific group of data.

Here’s an example of how to use nlsList to create predictions for multiple groups of data:

# Load required libraries
library(nlme)
library(nlmslist)

# Create a sample dataset
set.seed(123)
vi.sample <- data.frame(
  year = sample(c(2012, 2013, 2014), size = 100, replace = TRUE),
  julian = runif(100, min = 1, max = 365),
  site = sample(c("east", "west"), size = 100, replace = TRUE),
  type = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  trt = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  plot = sample(1:10, size = 100, replace = TRUE),
  vi = rnorm(100, mean = c(0.41, 0.44, 0.52, 0.64, 0.66, 0.67, 0.64, 0.66, 0.61, 0.7), sd = 0.1)
)

# Create an nlsList object
fit <- nlsList(object = vi.sample,
               formula = ~julian + I(year * site) + I(year * type) + I(year * trt) + I(plot),
               data = vi.sample,
               start = list(beta = 0, alpha = 1, sigma = 1))

# Print the nlsList object
print(fit)

Predicting Values with loess

Once we have created a loess fit for a specific group of data, we can use the predict function to make predictions on new data.

Here’s an example of how to predict values using loess:

# Load required libraries
library(loess)

# Create a sample dataset
set.seed(123)
vi.sample <- data.frame(
  year = sample(c(2012, 2013, 2014), size = 100, replace = TRUE),
  julian = runif(100, min = 1, max = 365),
  site = sample(c("east", "west"), size = 100, replace = TRUE),
  type = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  trt = sample(c("a", "b", "c"), size = 100, replace = TRUE),
  plot = sample(1:10, size = 100, replace = TRUE),
  vi = rnorm(100, mean = c(0.41, 0.44, 0.52, 0.64, 0.66, 0.67, 0.64, 0.66, 0.61, 0.7), sd = 0.1)
)

# Create a loess fit
fit <- loess(vi ~ julian, data = vi.sample)

# Predict values on new data
new_data <- data.frame(
  year = c(2012, 2013, 2014),
  julian = runif(3, min = 1, max = 365)
)

predictions <- predict(fit, newdata = new_data)

# Print the predictions
print(predictions)

Conclusion

In this tutorial, we have learned how to automate loess predictions using dlply and nlsList. We have also seen how to use the predict function to make predictions on new data. By following these steps, you can create your own automated loess prediction pipeline in R.

Last modified on 2024-10-17