A Fine Slice Of SQL Server
Steven Sanderson says it’s time:
The
ts()function in R is a fundamental tool for handling time series data. It takes four main arguments:
- data: A vector or matrix of time series values.
- start: The time of the first observation.
- end: The time of the last observation.
- frequency: The number of observations per unit of time.
Read on for an example of how this all works, as well as a function in the TidyDensity package to convert data into the R time series format.
Comments closedJohn MacKintosh wants to join on a greater than or less than operation:
For day 5, I had to create a function, and I’m writing this up, because it’s an example of a non-equi join between two tables.
In this particular sitation, there are are no common columns between the two tables, so my usualdata.tablehack of copying the columns of interest, renaming themjoin_col, and then keying them both does not work.
Click through for a working solution.
Comments closedSebastian Sauer takes us through a demo:
In this post, we’ll investigate the consequence of z-standardizing the predictor variables, and in addition the outcome variable in a simple logistic regression setting.
Do some coefficients change as a result of standardizing the values?
Click through for the example and what z-standardization does to the model.
Comments closedSteven 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.
Comments closedSteven 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.
Comments closedSteven 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.
Comments closedOsheen MacOscar wants to know how long it takes to start up that Shiny app:
In the last blog I spoke about using Google Lighthouse to test the speed of web pages. I wanted to build upon that and use Lighthouse to test some Shiny apps.
To get a feel for Shiny’s performance in a Lighthouse analysis, I needed a lot of shiny apps that I could test and create a dataset from, so I used the entries to the 2021 Shiny app contest, which is a competition where people enter Shiny apps to be judged on technical merit and artistic achievement. I used the 2021 apps as there has unfortunately not been a competition since. A full list of the submissions can be found on the Posit Community website.
Read on to see what you can do with Lighthouse, as well as a few pain points around it.
Comments closedSebastian 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.
Comments closedSteven 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 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.
Comments closedSteven 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.
Comments closed