Скачать или смотреть Changing Heat Maps: From Euclidean Distance to Custom Linkage Settings

Learn how to customize your heat map's distance and linkage settings for more accurate data representation in R!
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This video is based on the question https://stackoverflow.com/q/76299443/ asked by the user 'Rosie Lomas' ( https://stackoverflow.com/u/21918985/ ) and on the answer https://stackoverflow.com/a/76299536/ provided by the user 'Meisam' ( https://stackoverflow.com/u/19169296/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Customizing Heat Maps in R: Changing Distance and Linkage

Heat maps are powerful tools for visualizing data, especially in cases where you want to observe the patterns and relationships between multiple variables. Using the right distance metrics and linkage methods can significantly enhance the insights you gain from your heat map. In this guide, we'll explore how to change the default Euclidean Distance and Complete Linkage to other options when creating a heat map using R.

Understanding the Problem

In a recent inquiry, a user named Rosie was trying to customize her heat map using the milk dataset from the flexclust package in R. The goal was to adjust the distance measurement and clustering linkage to better suit her analysis needs for her homework assignment. By default, R’s heat map function uses Euclidean Distance and Complete Linkage, which might not always be the best choice for every dataset.

The Solution

To change the distance and linkage method in your heat map, R provides two main parameters: distfun for distance calculation and hclustfun for the method of hierarchical clustering. Here’s a step-by-step guide to customize these settings.

Step 1: Set Up Your Data

First, ensure your dataset is loaded properly. You can use the milk dataset from the flexclust package:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Using distfun for Custom Distance

Instead of the default Euclidean Distance, you can specify other distance metrics available in the stats::dist() function. Some common alternatives include:

Maximum: Identifies the maximum distance

Manhattan: Represents the sum of absolute differences

Canberra: A weighted version of the Manhattan distance

Binary: Measures the presence or absence of traits

Minkowski: A generalization of both Euclidean and Manhattan distances

For example, if you prefer using the maximum distance, your heat map code would look like this:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Clustering Method with hclustfun (Optional)

If you want to customize the hierarchical clustering method used for your heat map, you can adjust the hclustfun parameter. Some options include:

Single: Nearest neighbor method

Complete: Farthest neighbor method

Average: UPGMA method

Ward.D: Minimizes variance within clusters

You would simply add the hclustfun parameter in your code, like so:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By customizing the distance and linkage settings in your heat map, you can gain deeper insights and more accurately represent the relationships within your data. Don’t hesitate to explore other distance metrics and clustering methods that may fit your specific research needs better. Happy coding and data visualizing!