A Fine Slice Of SQL Server
John MacKintosh has eyes for .I:
In this post I’m using a small extract from the SIMD2020 dataset to figure out what the special operator
.Idoes.Files and code are on github if you’re interested
Click through for a demo-laden post. H/T R-Bloggers.
Comments closedSteven Sanderson takes us through time series creation:
R offers two primary time series classes:
- “ts”: Base R’s classic time series object, designed for regularly spaced data.
- “xts”: Part of the ‘xts’ package, offering enhanced flexibility and features.
Click through for examples using both of these methods.
Comments closedNorm Matloff answers a reader question:
In my December 22 blog, I first introduced the classic parametric quantile regression (QR) concept. I then showed how one could use the qeML package to perform quantile regression nonparametrically, using the package’s qeKNN function for a k-Nearest Neighbors approach. A reader then asked if this could be applied to random forests (RFs). The answer is yes, and this will be the topic of the current post.
Read on to learn more about how to do this, including some of the challenges you’ll face along the way. H/T R-Bloggers.
Comments closedHolger von Jouanne-Diedrich explains an important statistical concept we all too often forget:
In the realm of business and leadership, one statistical phenomenon often goes unrecognized yet significantly influences our understanding of performance and success. This is the concept of reversion to the mean (also called regression to the mean). This seemingly simple statistical occurrence can profoundly impact how we perceive management strategies, leadership effectiveness, and even the fate of those gracing the covers of prominent magazines. To understand what is going on, read on!
Read on for a video in German and an article in English, with some bonus R code to sell the story.
Comments closedSteven Sanderson builds some charts:
Our Flight Plan:
- Loading Up with Data: Grabbing our trusty dataset, AirPassengers.
- Taking Off with Base R: Creating a basic time series plot using base R functions.
- Soaring with ggplot2: Crafting a visually stunning time series plot using the ggplot2 library.
- Navigating Date Formatting: Customizing axis labels with scale_x_date() for clarity.
- Landing with Your Own Exploration: Encouraging you to take the controls and create your own time series plots!
Click through to see each of these steps in action.
Comments closedSteven 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 closed