Creating a Loop to Run Confirmatory Factor Analysis Models on Multiple Dataframes in R Using lapply() and for Loop

Creating a Loop to Complete Statistical Models on Multiple Dataframes in R

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Introduction

Statistical modeling is an essential aspect of data analysis, and R is one of the most popular programming languages for this task. In this article, we will explore how to create a loop to complete statistical models on multiple dataframes in R.

Background

Confirmatory Factor Analysis (CFA) is a widely used statistical technique for testing measurement models. It involves estimating a model that describes the relationships between observed and latent variables. In this article, we will focus on CFA using the lavaan package in R.

The Problem at Hand

We are given five confirmatory factor analysis models (OneFactor, TwoFactor, AlternativeTwoFactor, BiFactor, and BiFactorAlternativeFactor) and eleven separate dataframes (PHQ_Stroke_Author1 to PHQ_Stroke_Author11). We want to create a loop that can run these CFA models on each dataframe without having to repeat the same code for each one.

Creating a Loop to Run CFA Models on Multiple Dataframes

To solve this problem, we will use R’s built-in for loop and apply function syntax. We will also utilize the lapply() function from the base R package, which applies a given function to each element of an object.

First, let’s create our dataframes:

# Load required libraries
library(lavaan)
library(tidyverse)

# Create dataframes
dataframes <- list(
  PHQ_Stroke_Author1,
  PHQ_Stroke_Author2,
  PHQ_Stroke_Author3,
  PHQ_Stroke_Author4,
  PHQ_Stroke_Author5,
  PHQ_Stroke_Author6,
  PHQ_Stroke_Author7,
  PHQ_Stroke_Author8,
  PHQ_Stroke_Author9,
  PHQ_Stroke_Author10,
  PHQ_Stroke_Author11
)

Next, we will define our CFA models:

# Define CFA models
CFA_Models <- list(
  OneFactor = "WLS",
  TwoFactor = "WLS",
  AlternativeTwoFactor = "WLS",
  BiFactor = "WLS",
  BiFactorAlternativeFactor = "WLS"
)

Using `lapply()` to Run CFA Models on Multiple Dataframes

Now, let’s use lapply() to run our CFA models on each dataframe:

# Use lapply() to run CFA models on multiple dataframes
CFA_Fit <- lapply(dataframes, function(x) {
  CFA_Models$OneFactor <- cfa(CFA_Models$OneFactor, data = x, estimator = "WLS")
  CFA_Models$TwoFactor <- cfa(CFA_Models$TwoFactor, data = x, estimator = "WLS")
  CFA_Models$AlternativeTwoFactor <- cfa(CFA_Models$AlternativeTwoFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactor <- cfa(CFA_Models$BiFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactorAlternativeFactor <- cfa(CFA_Models$BiFactorAlternativeFactor, data = x, estimator = "WLS")
  return(list(OneFactor = CFA_Models$OneFactor, TwoFactor = CFA_Models$TwoFactor,
               AlternativeTwoFactor = CFA_Models$AlternativeTwoFactor, BiFactor = CFA_Models$BiFactor,
               BiFactorAlternativeFactor = CFA_Models$BiFactorAlternativeFactor))
})

Using `for` Loop to Run CFA Models on Multiple Dataframes

Alternatively, we can use a for loop to run our CFA models on each dataframe:

# Use for loop to run CFA models on multiple dataframes
CFA_Fit <- vector("list", length(dataframes))
for (i in seq_along(dataframes)) {
  x <- dataframes[[i]]
  CFA_Models$OneFactor <- cfa(CFA_Models$OneFactor, data = x, estimator = "WLS")
  CFA_Models$TwoFactor <- cfa(CFA_Models$TwoFactor, data = x, estimator = "WLS")
  CFA_Models$AlternativeTwoFactor <- cfa(CFA_Models$AlternativeTwoFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactor <- cfa(CFA_Models$BiFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactorAlternativeFactor <- cfa(CFA_Models$BiFactorAlternativeFactor, data = x, estimator = "WLS")
  CFA_Fit[[i]] <- list(OneFactor = CFA_Models$OneFactor, TwoFactor = CFA_Models$TwoFactor,
                        AlternativeTwoFactor = CFA_Models$AlternativeTwoFactor, BiFactor = CFA_Models$BiFactor,
                        BiFactorAlternativeFactor = CFA_Models$BiFactorAlternativeFactor)
}

Example Use Case

Here is an example use case where we create a dataframe and run the CFA models on it:

# Create a new dataframe
new_data <- data.frame(
  Sepal.Length = rnorm(150),
  Sepal.Width = rnorm(150),
  Species = sample(c("Setosa", "Versicolor", "Virginica"), 150, replace = TRUE)
)

# Use for loop to run CFA models on the new dataframe
CFA_Fit <- vector("list", length(new_data))
for (i in seq_along(new_data)) {
  x <- new_data[[i]]
  CFA_Models$OneFactor <- cfa(CFA_Models$OneFactor, data = x, estimator = "WLS")
  CFA_Models$TwoFactor <- cfa(CFA_Models$TwoFactor, data = x, estimator = "WLS")
  CFA_Models$AlternativeTwoFactor <- cfa(CFA_Models$AlternativeTwoFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactor <- cfa(CFA_Models$BiFactor, data = x, estimator = "WLS")
  CFA_Models$BiFactorAlternativeFactor <- cfa(CFA_Models$BiFactorAlternativeFactor, data = x, estimator = "WLS")
  CFA_Fit[[i]] <- list(OneFactor = CFA_Models$OneFactor, TwoFactor = CFA_Models$TwoFactor,
                        AlternativeTwoFactor = CFA_Models$AlternativeTwoFactor, BiFactor = CFA_Models$BiFactor,
                        BiFactorAlternativeFactor = CFA_Models$BiFactorAlternativeFactor)
}

Conclusion

In this article, we explored how to create a loop to complete statistical models on multiple dataframes in R. We used lapply() and for loops to run our CFA models on each dataframe without having to repeat the same code for each one. This technique can be useful when working with large datasets or when you need to perform repetitive tasks.

Last modified on 2024-02-26