Changing the Reference Level Used in Logistic Regression (GLM) in R

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Logistic regression is a widely used statistical technique for modeling binary outcomes. In R, the glm function is commonly used to perform logistic regression analysis. However, one common issue users face is changing the reference level used by R when running the glm function.

In this blog post, we will delve into the details of how to change the reference level used in logistic regression (GLM) in R, including using the relevel() function and alternative libraries such as forcats.

Understanding Reference Levels in Logistic Regression

When running a logistic regression analysis in R, the glm function automatically determines which level of the predictor variable to use as the reference level. This is often done based on the order of levels or alphabetical ordering.

For example, consider a binary outcome variable trait and a predictor variable sex. The reference level for sex might be automatically set to “male” (the first level) or “female” (the second level). However, in some cases, we may want to use a different level as the reference.

Using relevel() to Change Reference Levels

One way to change the reference level used by R is by using the relevel() function. The relevel() function allows us to reassign levels of a factor variable.

Here’s an example:

# Load required libraries
library(dplyr)

# Create a sample dataset
data <- data.frame(
  trait = c("yes", "no", "yes", "no"),
  sex = c("male", "female", "male", "female")
)

# Perform logistic regression analysis using glm
modelA <- glm(trait ~ sex, data = data, family = binomial(link = 'logit'))

# Change the reference level for sex to female
relevel(modelA$sex, 2)

# Perform logistic regression analysis again with new reference levels
modelB <- glm(trait ~ sex, data = data, family = binomial(link = 'logit'))

In this example, we use relevel() to change the reference level for the sex variable to “female” (the second level). We then perform logistic regression analysis again using the new reference levels.

Using fct_relevel() from the forcats Library

Another way to change reference levels is by using the fct_relevel() function from the forcats library. This function provides a more convenient and flexible way to reassign levels of factor variables.

Here’s an example:

# Load required libraries
library(forcats)

# Create a sample dataset
data <- data.frame(
  trait = c("yes", "no", "yes", "no"),
  sex = c("male", "female", "male", "female")
)

# Convert the factor variable to categorical using fct_cscale()
data$sex <- as_factor(data$sex, labels = c("female", "male"))

# Use fct_relevel() to change the reference level for sex
data$sex <- fct_relevel(data$sex, 2)

# Perform logistic regression analysis using glm
modelC <- glm(trait ~ sex, data = data, family = binomial(link = 'logit'))

In this example, we use fct_cscale() to convert the sex variable to categorical and then use fct_relevel() to change the reference level for sex.

Additional Considerations

When changing reference levels, it’s essential to consider the implications on the model fit. In some cases, using a different reference level might affect the model coefficients or interpretability.

It’s also worth noting that R provides several other functions and libraries (such as reorder() and ordered) for working with ordered factors. These can be used in conjunction with relevel() to achieve more complex reassignments of levels.

Case Study: Applying Reference Level Changes

Let’s consider a case study where we want to apply reference level changes using both the relevel() function and the fct_relevel() function from the forcats library.

Suppose we have a dataset containing information about patients with a particular disease, including their genotype (A or B) and age group (young or old). We want to perform logistic regression analysis to predict whether a patient will develop the disease based on their genotype and age group. The reference level for the genotype variable might be automatically set to “B” (the first level), but we would like to use the “A” allele as the reference.

We can apply the relevel() function to reassign the levels of the genotype variable:

# Load required libraries
library(dplyr)

# Create a sample dataset
data <- data.frame(
  disease = c("yes", "no", "yes", "no"),
  genotype = c("A", "B", "A", "B"),
  age_group = c("young", "old", "young", "old")
)

# Perform logistic regression analysis using glm
modelD <- glm(disease ~ genotype + age_group, data = data, family = binomial(link = 'logit'))

# Change the reference level for genotype to A
relevel(modelD$genotype, 1)

Alternatively, we can use the fct_relevel() function from the forcats library:

# Load required libraries
library(forcats)

# Create a sample dataset
data <- data.frame(
  disease = c("yes", "no", "yes", "no"),
  genotype = c("A", "B", "A", "B"),
  age_group = c("young", "old", "young", "old")
)

# Convert the factor variable to categorical using fct_cscale()
data$genotype <- as_factor(data$genotype, labels = c("B", "A"))

# Use fct_relevel() to change the reference level for genotype
data$genotype <- fct_relevel(data$genotype, 2)

# Perform logistic regression analysis using glm
modelE <- glm(disease ~ genotype + age_group, data = data, family = binomial(link = 'logit'))

In both examples, we apply the relevel() function to reassign the levels of the genotype variable. The resulting model coefficients and p-values will be different from those obtained using the default reference level.

Conclusion

Changing reference levels in logistic regression (GLM) can be achieved using the relevel() function or alternative libraries such as forcats. Both approaches provide a way to reassign levels of factor variables, allowing for more flexibility and control over the model fit. However, it’s essential to consider the implications on the model coefficients and interpretability when making these changes.

In this blog post, we have explored how to change reference levels in logistic regression using both relevel() and fct_relevel(). We have also provided case studies demonstrating the application of these techniques to real-world datasets. By understanding how to manipulate reference levels, you can gain more insight into your data and improve the accuracy of your models.

Last modified on 2024-06-19