Category: R

Steven Sanderson talks about a discrete form of regression:

Hey data enthusiasts! Today, we’re diving into the fascinating world of count data and its trusty sidekick, Poisson regression. Buckle up, because we’re about to explore how this statistical powerhouse helps us understand the factors influencing, you guessed it, counts.

Scenario: Imagine you’re an education researcher, eager to understand how a student’s GPA might influence their job offer count after graduation. But hold on, job offers aren’t continuous – they’re discrete, ranging from 0 to a handful. That’s where Poisson regression comes in!

I have an unhealthy love for Poisson techniques, so I highly recommend checking this out.

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Steven Sanderson explains the concept of an odds ratio:

Imagine a loan officer flipping a coin to decide whether to approve your loan. Odds ratios tell you how much more likely one factor (like your income) makes the “heads” (approval) side appear compared to another (like your student status).

In logistic regression, odds ratios compare the odds of an event (loan default, in our case) for two groups defined by a specific variable. They’re like multipliers: greater than 1 means something increases the chances of default, while less than 1 means it decreases them.

As for why, we use odds ratios because it’s hard to track and interpret changes in probabilities directly, at least when you’re thinking small numbers.

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Steven Sanderson deals with my favorite failure of BLUE (mainly because I love the name):

Tired of your least-squares regression model giving wonky results because some data points shout louder than others? Meet Weighted Least Squares (WLS), the superhero of regression, ready to tackle unequal variance (heteroscedasticity) and give your model the justice it deserves! Today, we’ll dive into the world of WLS in R, using base functions for maximum transparency. Buckle up, data warriors!

Read on to see how Weighted Least Squares helps in data analysis when you have heteroskedasticity.

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Sebastian Sauer performs some tests:

Bayes models (using MCMC) build on drawing random numbers. By their very nature, random numbers are random. Unless they are not. As you may know, the random number fuctions in computers are purely deterministic.

However, in practice, some inpredictable behavior may still show up. The reason being simply that two computational environment must – in theory – being exactly identical in order to reproduce the same results. At least identical in every bit that influence random number generation.

Click through for the testbed and code.

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Steven Sanderson takes us through two regression techniques. First up is stepwise regression:

Stepwise regression is a powerful technique used to build predictive models by iteratively adding or removing variables based on statistical criteria. In R, this can be achieved using functions like step() or manually with forward and backward selection.

Piecewise regression follows:

Piecewise regression is a powerful technique that allows us to model distinct segments of a dataset with different linear relationships. It’s like fitting multiple straight lines to capture the nuances of different regions in your data. So, grab your virtual lab coat, and let’s get started.

Read on for explanations of both techniques, as well as some visuals and potential pitfalls you might run into along the way.

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Steven Sanderson performs a different form of regression:

Spline regression is particularly useful when the relationship between the independent and dependent variables is not adequately captured by a linear model. It involves fitting a piecewise continuous curve (spline) to the data. Let’s dive into the process using R.

Click through and you’ll be reticulating splines just like it’s Sim City 2000.

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Peter Laurinec builds a model to test a model:

In the last blog post about Multistep forecasting losses, I showed the usage of the fantastic method adam from the smooth R package on household electricity consumption data, and compared it with benchmarks.

Since I computed predictions from 10 methods/models for a long period of time, it would be nice to create some ensemble models for precise prediction for our household consumption data. For that purpose, it would be great to predict for example future errors of these methods. It is used in some known ensemble methods, which are not direct about stacking. Predicting errors can be beneficial for prediction weighting or for predicting the rank of methods (i.e. best one prediction). For the sake of learning something new, I will try multivariate regression models, so learning from multiple targets at once. At least, it has the benefit of simplicity, that we need only one model for all base prediction models.

Click through for Peter’s process. H/T R-Bloggers.

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Steven Sanderson performs quantile regression:

Quantile regression is a robust statistical method that goes beyond traditional linear regression by allowing us to model the relationship between variables at different quantiles of the response distribution. In this blog post, we’ll explore how to perform quantile regression in R using the quantreg library.

If you need to hone up on your quantile regression knowledge, Wikipedia is usually good for statistics and here’s an academic paper from Roger Koenker and Kevin Hallock on the topic.

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