Manually Creating a Formula in R: A Deep Dive into pglm and Non-Standard Evaluation

Introduction

As a data analyst or statistician, working with regression models is an essential part of our daily tasks. One of the most commonly used libraries for performing linear and generalized linear regression is the pglm package in R. However, when it comes to creating formulas for these models, things can get tricky due to the way pglm captures its arguments using non-standard evaluation.

In this article, we will delve into the world of pglm, explore how it handles formulas, and provide a step-by-step guide on how to create formulas manually. We will also discuss the importance of understanding non-standard evaluation in R and its implications for data analysis.

What is Non-Standard Evaluation?

Non-standard evaluation (NSE) is a feature in R that allows us to pass expressions as arguments to functions instead of just their values. This can be useful when working with complex formulas or expressions, but it also introduces some challenges when dealing with certain libraries like pglm.

When we use the ~ operator to specify a formula in R, it is actually creating an expression that needs to be evaluated before being passed to the model. However, pglm does not expect this evaluation step to happen automatically. Instead, it relies on us to provide the correct formula as a string.

The Problem with Manual Formula Creation

The problem arises when we try to create formulas manually using strings. As shown in the provided example, creating a formula like "union ~ wage + exper" can lead to errors because pglm expects a list of variables rather than a single variable name followed by an arithmetic operator.

To illustrate this, let’s take a closer look at how pglm captures its arguments:

# Example: Creating a formula using the ~ operator
formula <- paste0(names(y), "~", form)

As you can see, the paste0 function is used to combine the variable names with an arithmetic operator (~). However, this expression needs to be evaluated before being passed to pglm, which can lead to issues.

A Solution Using do.call and pglm_env

To overcome these challenges, we can use a combination of the do.call function and the list2env function. Here’s an example of how we can modify our function to create formulas manually:

# Modified fit_panel_binary function
fit_panel_binary <- function(y, x) {
  # Create data frame with y and x variables
  x <- cbind(x, y)
  
  # Get variable names from data frame
  varnames <- names(x)[3:(length(x))]
  
  # Exclude dependent variable from varnames
  varnames <- varnames[!(varnames == names(y))]
  
  # Create formula as a string
  form <- paste0(varnames, collapse = "+")
  
  # Convert the string to a formula using list2env
  pglm_env <- list2env(list(formula = paste(names(y), "~", form)), envir = new.env())
  
  # Use do.call to call pglm with the formula and data
  model <- do.call(pglm, params, envir = pglm_env)
}

In this modified version of our function, we first create a string representing the formula using paste0. However, instead of trying to evaluate this expression automatically, we use the list2env function to convert it into an environment variable named formula. This allows us to pass the correct formula to pglm.

We then use the do.call function to call pglm with the formula and data. By specifying the envir = pglm_env argument, we ensure that the correct environment variables are passed to pglm, which can help resolve any issues related to non-standard evaluation.

Conclusion

In this article, we explored how to create formulas manually in R using the pglm package. We discussed the challenges of non-standard evaluation and provided a step-by-step guide on how to overcome them using do.call and list2env. By understanding how these functions work together, you can create complex formulas with ease and ensure that your models are executed correctly.

Example Use Cases

Here’s an example of how you might use the modified fit_panel_binary function:

# Load necessary libraries
library(pglm)

# Create a data frame with y and x variables
data <- data.frame(
  union = c(1, 2, 3),
  wage = c(10, 20, 30),
  exper = c(5, 10, 15)
)

# Fit the model using the modified function
model <- fit_panel_binary(data$union, data[, 2:4])

# Print the results
summary(model)

In this example, we create a data frame with y and x variables and use the modified fit_panel_binary function to fit the linear regression model. We then print the summary of the model using the summary function.

Note that the output will vary depending on the values in your data.

Last modified on 2025-01-23