Understanding the Rjags Error Message: Dimension Mismatch

Introduction to Bayesian Analysis with JAGS

Bayesian analysis is a powerful statistical approach that allows us to update our beliefs about a population based on new data. In this article, we will explore how to perform Bayesian analysis using the JAGS (Just Another Gibbs Sampler) software, specifically focusing on addressing the error message “Dimension mismatch” that can occur when working with categorical variables.

Background: Understanding JAGS

JAGS is a probabilistic programming language used for Bayesian modeling. It allows us to specify our model in R and then use it to generate samples from the posterior distribution of the parameters. The rjags package provides an interface to JAGS, making it easier to work with.

Understanding the Problem

The problem arises when we try to fit a Bayesian model using JAGS but encounter an error message indicating “Dimension mismatch.” This error occurs because there is confusion about the dimension of one or more variables in our model. In this case, the variable theta seems to be causing issues.

Identifying the Cause

The problem statement includes the following lines of code:

model{
  for(i in 1:Ntotal){
    y[i] ~ dbern(theta[s[i]])
  }
  for(s in 1:Nsubj){
    theta[s] ~ dbeta(2,2)
  }
}

As suggested by @nicola, the problem is that we’re passing s as data to our model but also using it as a counter iterating over 1:Nsubj. This causes confusion about the dimension of theta, which has length 15 or 2?

Resolving the Issue

To resolve this issue, we need to change the indexing used in our model. We should use j instead of s when iterating over Nsubj.

model{
  for(i in 1:Ntotal){
    y[i] ~ dbern(theta[s[i]])
  }
  for(j in 1:Nsubj){
    theta[j] ~ dbeta(2,2)
  }
}

By making this change, we ensure that theta has the correct dimension.

Additional Considerations

When working with categorical variables in JAGS, it’s essential to understand the indexing used by the model. Using the correct index can prevent errors like “Dimension mismatch” and ensure a smooth modeling process.

In addition to resolving the error message caused by incorrect indexing, consider the following best practices when using JAGS:

1. Clearly define your model: Ensure that your model is well-defined and easy to understand.
2. Use meaningful variable names: Choose descriptive variable names for better readability.
3. Test your model: Verify that your model works correctly by checking its output.

Example Use Case

Let’s consider a simple example where we want to fit a Bayesian linear regression model using JAGS.

# Load necessary libraries
library(rjags)
library(runjags)

# Define the data
set.seed(123)
x <- rnorm(100, mean = 1, sd = 2)
y <- 3 + 4 * x + rnorm(100, sd = 0.5)
data <- data.frame(y, x)

# Define the model
modelString="
model{
  for(i in 1:N){
    y[i] ~ dnorm(mu + sigma * x[i])
    mu ~ dnorm(0, tau)
    sigma ~ dnorm(0.01, tau)
    tau ~ dexp(1/10)
  }
}
"

# Write the model to a file
writeLines(modelString, con="LINEARMODEL.txt")

# Fit the model
jagsModel <- jags.model(file="LINEARMODEL.txt", data=data)

# Print the summary
summary(jagsModel)

In this example, we fit a simple linear regression model using JAGS. The modelString variable defines our model, and the rest of the code follows standard best practices for working with JAGS.

Conclusion

The “Dimension mismatch” error message can be resolved by understanding how to correctly index variables in our Bayesian models. By following best practices for defining your model and using meaningful variable names, you can create robust and interpretable Bayesian models.

Last modified on 2025-04-01