We are happily introducing a new k-means clustering algorithm that includes a powerful multiple missing data imputation at the computational cost of a few extra random imputations (benchmarks following in a separate article). More precisely, the algorithm draws the missing values iteratively based on the current cluster assignment so that ...

It is usually said, that for– and while-loops should be avoided in R. I was curious about just how the different alternatives compare in terms of speed. The first loop is perhaps the worst I can think of – the return vector is initialized without type and length so that the ...

Do you want to optimise your code but don't know where to start? In this post I guide you through my thought process when I optimised my code. Der Beitrag Optimising your R code – a guided example erschien zuerst auf STATWORX.

Recently, I had the opportunity to showcase tidymodels in workshops and talks. Because of my vantage point as a user, I figured it would be valuable to share what I have learned so far. Let’s begin by framing where tidymodels fits in our analysis projects. The diagram above is ...

Continuing on the below post, I am going to use a gradient boosted machine model to predict combined miles per gallon for all 2019 motor vehicles. Part 1: Using Decision Trees and Random Forest to Predict MPG for 2019 Vehicles The raw data is located on the EPA government siteThe variables/features I ...

Continuing on the below post, I am going to use a gradient boosted machine model to predict combined miles per gallon for all 2019 motor vehicles. Part 1: Using Decision Trees and Random Forest to Predict MPG for 2019 Vehicles The raw data is located on the EPA government siteThe variables/features I ...

In this note, we discuss the use of Cohen’s D for planning difference-of-mean experiments. Estimating sample size Let’s imagine you are testing a new weight loss program and comparing it so some existing weight loss regimen. You want to run an experiment to determine if the new program ...

It has been only two months since I summarized my reviews of point-and-click front ends for R, and it’s already out of date! I have converted that post into a regularly-updated article and added a plot of total features, which … Continue reading →

In this article, we discuss the basics of ordinal logistic regression and its implementation in R. Ordinal logistic regression is a widely used classification method, with applications in variety of domains. This method is the go-to tool when there is a natural ordering in the dependent variable. For example, dependent ...

Whenever I’m developing R code or writing data wrangling or analysis scripts for research projects that I work on I use Emacs and its add-on package Emacs Speaks Statistics (ESS). I’ve done so for nigh on a couple of decades now, ever since I switched full time to ...

Introduction Survival distributions Shapes of hazard functions Exponential distribution Weibull distribution (AFT) Weibull distribution (PH) Gompertz distribution Gamma distribution Lognormal distribution Log-logistic distribution Generalized gamma distribution Regression Intercept only model Adding covariates Conclusion Introduction Survival analysis is used to analyze the time until the occurrence of an event (or multiple ...

by Mariluz Congosto Network analysis offers a perspective of the data that broadens and enriches any investigation. Many times we deal with data in which the elements are related, but we have them in a tabulated format that is difficult to import into network analysis tools. Relationship data require a ...