Understanding Error Messages in Model-Based Clustering

When working with machine learning and statistical modeling, it’s common to encounter error messages that can be confusing and difficult to interpret. In this article, we’ll delve into the specific error message you’re experiencing when transforming a scale gives an infinite value, both on the y-axis and x-axis.

Background: Model-Based Clustering

Model-based clustering is a type of unsupervised learning where we use a probabilistic model to cluster data points into different groups. In this case, we’re using the Mclust package in R, which implements various Gaussian mixture models for clustering.

Mclust allows us to specify the number of components and the parameters that define the model. The fviz_cluster function from the fviz package is used to create a scatterplot of the clustered data points.

Error Messages

The error messages you’re seeing are related to the transformation of the x-axis and y-axis scales in your scatterplot. Let’s break down each message:

1. NaNs produced: This indicates that there are NaN (Not a Number) values being produced during the transformation process.
2. Transformation introduced infinite values in continuous x-axis: This message suggests that the transformation is introducing infinite values on the x-axis, which can cause issues with plotting.
3. Transformation introduced infinite values in continuous y-axis: Similar to the previous message, this indicates that infinite values are being introduced on the y-axis.

Cause of the Error

The root cause of these errors lies in the way we’re transforming the scales using logarithmic functions.

In the scale_x_log10 and scale_y_log10 functions, we’re using the trans_breaks function to specify the breaks for the log scale. The math_format function is used to define the formatting of the labels on the x-axis and y-axis.

However, when dealing with infinite values, these functions can behave unexpectedly. Specifically, when a value approaches infinity, the logarithmic transformation becomes increasingly large, causing numerical instability issues.

Solution

To resolve this issue, we need to be mindful of how we’re handling infinite values during the transformation process.

Here are some steps you can take:

1. Check for missing values: Make sure that there are no missing values in your data points that could be causing the NaNs.
2. Use a robust transformation: Instead of using logarithmic transformations, consider using a more robust method such as the sqrt function or a robust regression approach like the Huber-White standard error estimator.
3. Clip infinite values: When dealing with infinite values, consider clipping them to prevent numerical instability issues.

Code Example

Let’s demonstrate how you can modify your code to handle infinite values using the clip function:

# Clip infinite values in the data
dots.Mclust <- dots %>% 
  clip(x = Inf, y = Inf)

# Update the scale functions
visual <- fviz_cluster(dots.Mclust, 
                       ellipse = FALSE, 
                       shape = 20, 
                       ellipse.alpha = 0.1,
                       alpha = 0.450, 
                       geom = c("point"),
                       show.clust.cent = FALSE,
                       main = FALSE,
                       legend = c("right"),
                       palette = "npg",
                       legend.title = "Clusters"
                      ) + 
  labs(x = "Green Fluorescence Intensity", y = "Red Fluorescence Intensity") +
  scale_x_log10(breaks = trans_breaks("log10", function(x) min(1000, 10^x)), # Clip at 1000
                labels = trans_format("log10", math_format(min(2, 10^.x))), # Scale x-axis labels to (1,2]
                limits = c(1,1e4)) +
  scale_y_log10(breaks = trans_breaks("log10", function(x) min(1000, 10^x)), # Clip at 1000
                labels = trans_format("log10", math_format(min(2, 10^.x))), # Scale y-axis labels to (1,2]
                limits = c(1,1e3))

By clipping the infinite values in the data and adjusting the scale functions accordingly, we can prevent numerical instability issues and produce a more accurate scatterplot.

Conclusion

Error messages like these can be challenging to interpret, but by understanding the underlying causes and taking steps to mitigate them, you can create high-quality visualizations that effectively communicate your results. Remember to check for missing values, use robust transformations, and clip infinite values to ensure accuracy in your scatterplots.

Last modified on 2024-10-25