Category: Data Science

Keith Goldfield updates the simstudy package:

A researcher reached out to me a few weeks ago. They were trying to generate longitudinal data that included irregularly spaced follow-up periods. The default periods generated by the function addPeriods in the simstudy package are {0,1,2,…,n−1}{0,1,2,…,n−1}, where there are n total periods. However, when follow-up periods required more specificity, such as {0,90,180,365}{0,90,180,365} days from baseline, users had to manually add them. Originally, I had intended to incorporate this feature into the function, but unfortunately it slipped through the cracks. Thanks to the clear motivation provided by the researcher, I’ve implemented this enhancement. Users can now replace the default vector with their desired set of follow-up periods using the new argument periodVec. This addition is available in the development version of simstudy on GitHub.

Read on to see how it works. H/T R-Bloggers.

Steven Sanderson performs a test:

In the world of statistics and data analysis, understanding and accurately estimating the parameters of probability distributions is crucial. One such distribution is the chi-square distribution, often encountered in various statistical analyses. In this blog post, we’ll dive into how we can estimate the degrees of freedom (“df”) and the non-centrality parameter (“ncp”) of a chi-square distribution using R programming language.

Read on to learn more about the process of estimation while I grumble something about Bayesian analysis being better.

Steven Sanderson is from the 5th dimension:

Visualizing similarities between data points can be tricky, especially when dealing with many features. This is where multidimensional scaling (MDS) comes in handy. It allows us to explore these relationships in a lower-dimensional space, typically 2D or 3D for easier interpretation. In R, the cmdscale() function from base R and is a great tool for performing classical MDS.

Click through to see how this works. In case you’re curious, cmdscale() is an example of principal coordinates analysis. If you’re familiar with principal components analysis, that’s a different form of multidimensional scaling.

Steven Sanderson says, act normal:

Data normalization is a crucial preprocessing step in data analysis and machine learning workflows. It helps in standardizing the scale of numeric features, ensuring fair treatment to all variables regardless of their magnitude. In this tutorial, we’ll explore how to normalize data in R using practical examples and step-by-step explanations.

Read on for a definition of what this means and how you can do it.

Steven Sanderson has achieved normality:

Before we dive into the code, let’s understand the concept behind quantile normalization. At its core, quantile normalization aims to equalize the distributions of multiple datasets by aligning their quantiles. This ensures that each dataset has the same distribution of values, making meaningful comparisons possible.

This is a bit different from normalizing individual data points in one dataset, as you can see in the post.

Steven Sanderson takes a sample:

The sample() function in R is a powerful tool that allows you to generate random samples from a given dataset or vector. It’s an essential function for tasks such as data analysis, Monte Carlo simulations, and randomized experiments. In this blog post, we’ll explore the sample() function in detail and provide examples to help you understand how to use it effectively.

Read on to see what options are available with sample() and the different ways in which you can use the function.