Category: Data Science

Steven Sanderson doesn’t need all of those data points:

Data visualization is a powerful tool for gaining insights from your data. In R, you have a plethora of libraries and functions at your disposal to create stunning and informative plots. One common task is to plot a subset of your data, which allows you to focus on specific aspects or trends within your dataset. In this blog post, we’ll explore various techniques to plot subsets of data in R, and I’ll explain each step in simple terms. Don’t worry if you’re new to R – by the end of this post, you’ll be equipped to create customized plots with ease!

Click through for several techniques for subsetting data, as well as reasons why you might want to do it.

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Adrian Tam tries out a couple of tests:

R as a data analytics platform is expected to have a lot of support for various statistical tests. In this post, you are going to see how you can run statistical tests using the built-in functions in R. Specifically, you are going to learn:

• What is t-test and how to do it in R
• What is F-test and how to do it in R

This is one of the things that R does best among any language: statistical testing. R has support for an enormous number of statistical functions, either built into the base language or available as packages.

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John Mount picks up on a prior post:

For this note, let’s work out how to directly try and overcome the omitted variable bias by solving for the hidden or unobserved detailed data. We will work our example in R. We will derive some deep results out of a simple set-up. We show how to “un-marginalize” or “un-summarize” data.

This is an interesting dive into a common problem, and something which we can easily work around in linear regression, but not in logistic regression.

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Adrian Tam rolls the dice:

Whether working on a machine learning project, a simulation, or other models, you need to generate random numbers in your code. R as a programming language, has several functions for random number generation. In this post, you will learn about them and see how they can be used in a larger program. Specifically, you will learn

• How to generate Gaussian random numbers into a vector
• How to generate uniform random numbers
• How to manipulate random vectors and random matrices

And, of course, these are pseudo-random numbers because we’re still dealing with computers and random seeds, after all.

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John Mount misses a variable:

I would like to illustrate a way which omitted variables interfere in logistic regression inference (or coefficient estimation). These effects are different than what is seen in linear regression, and possibly different than some expectations or intuitions.

This is an interesting article and there’s a really good comment helping to explain this effect in epidemiology.

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