Creating an Object out of the `preProcess` Function in R

Introduction

The caret package in R provides a comprehensive set of functions for building, evaluating, and tuning regression models. One of these functions is preProcess, which preprocesses a dataset by scaling and centering its variables. In this article, we will explore how to create an object out of the preProcess function.

Background

The preProcess function from the caret package takes a numeric matrix (X) as input and returns a preprocessed version of it. The preprocessed version is then used as the input for other models in the caret package, such as logistic regression, decision trees, and random forests.

Here’s a brief overview of how the function works:

It scales the variables using the Standardize method.
It centers the variables using the Center method.

Scaling

Scaling involves converting the data to have zero mean and unit variance. This is useful for many machine learning algorithms, as it ensures that all features are on the same scale.

The preProcess function uses the Standardize method for scaling. This method subtracts the mean of each variable from its values and then divides by the standard deviation.

Centering

Centering involves subtracting a constant value from each variable to set its mean to zero. However, in many cases, centering can lead to issues with certain machine learning algorithms, especially those that are sensitive to the intercept term.

The preProcess function uses the Center method for centering. This method subtracts the mean of each variable from its values.

Preprocessing Steps

To create an object out of the preProcess function, we need to understand how it preprocesses data. Here’s a step-by-step guide:

Define your dataset
Create a function that uses the preProcess function
Call the function and store its output in an object

Step 1: Define Your Dataset

The first step in using the preProcess function is to define your dataset. In this case, we are working with two datasets (dt1 and dt2). We can use these datasets as input for our preProcess function.

# Load necessary libraries
library(caret)

# Define the dataset
dt1 <- data.frame(
    X = c(1, 2, 3),
    Y = c(4, 5, 6)
)

dt2 <- data.frame(
    X = c(7, 8, 9),
    Y = c(10, 11, 12)
)

Step 2: Create a Function that Uses the `preProcess` Function

Now that we have defined our dataset, let’s create a function that uses the preProcess function. We will define this function within a new function called my_func.

# Define my_func
my_func <- function(dt1, dt2, norm = "spatialSign") {
    # Create preprocessed datasets for both models
    X <- dt1[, -ncol(dt1)]
    Y <- dt1[, ncol(dt1)]

    t <- holdOut(Y, ratio = 8/10, mode = "random")

    prepr <- preProcess(X[t$tr, ], method = norm)

    # Return the preprocessed datasets
    list(preprocessed_X = X[t$tr, ], preprocessed_Y = Y[t$tr, ], preprocessed_X_test = X[!t$tr, ], preprocessed_Y_test = Y[!t$tr, ])
}

Step 3: Call the Function and Store Its Output in an Object

Finally, let’s call our my_func function and store its output in an object called my_outcome.

# Call my_func and store its output in an object
my_outcome <- my_func(dt1, dt2)

# Print the contents of my_outcome
print(my_outcome)

Alternative Approach: Using Global Variables

Another way to create an object out of the preProcess function is by assigning it a global variable.

# Define my_func
my_func <- function(dt1, dt2, norm = "spatialSign") {
    # Create preprocessed datasets for both models
    X <- dt1[, -ncol(dt1)]
    Y <- dt1[, ncol(dt1)]

    t <- holdOut(Y, ratio = 8/10, mode = "random")

    prepr <- preProcess(X[t$tr, ], method = norm)

    # Return the preprocessed datasets
    list(preprocessed_X = X[t$tr, ], preprocessed_Y = Y[t$tr, ], preprocessed_X_test = X[!t$tr, ], preprocessed_Y_test = Y[!t$tr, ])
}

# Assign local variable to a global variable
my_func <- function(dt1, dt2, norm = "spatialSign") {
    # Create preprocessed datasets for both models
    X <- dt1[, -ncol(dt1)]
    Y <- dt1[, ncol(dt1)]

    t <- holdOut(Y, ratio = 8/10, mode = "random")

    global(prepr) <- preProcess(X[t$tr, ], method = norm)

    # Return the preprocessed datasets
    list(preprocessed_X = X[t$tr, ], preprocessed_Y = Y[t$tr, ], preprocessed_X_test = X[!t$tr, ], preprocessed_Y_test = Y[!t$tr, ])
}

Conclusion

In this article, we explored how to create an object out of the preProcess function in R. We defined a function called my_func, which used the preProcess function to preprocess data for two models. We also discussed alternative approaches to creating objects from this function.

By following these steps and using our examples as a guide, you can now create your own functions that use the preProcess function in R.

Recommendations

Use the first approach of defining a local variable within a function instead of assigning it a global variable.
If you need to reuse this preprocessed data for multiple models, consider using the alternative approach with global variables.
Be aware that using global variables can sometimes lead to unexpected behavior and should be used with caution.

Additional Resources

For more information on functions in R, including those from the caret package, please refer to:

Functions by CRAN.
preProcess by CRAN.
caret by CRAN.

Last modified on 2023-09-06

Creating an Object out of the preProcess Function in R