Gavin Simpson
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gsimpson.bsky.social
Gavin Simpson
@gsimpson.bsky.social
2.6K followers 700 following 110 posts
(Palaeo)[ecologist | limnologist] & #fakeStatistican, #rstats user, wielder of #GAMs. He/him/his. Opinions mine…
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Reposted by Gavin Simpson
robinince.bsky.social
Here is a little piece about the day of my final Monkey Cage recording - a couple of weeks ago in beautiful #manchester www.bigissue.com/opinion/robi...
December 26, 2025 at 5:07 PM
Reposted by Gavin Simpson
robinince.bsky.social
Goodbye, Monkey Cage - not as infinite as I imagined
December 25, 2025 at 11:55 AM
gsimpson.bsky.social
Probably should have been made a month or more ago, but better late than never. Made my mum’s family Christmas cake recipe for the first time since moving to 🇩🇰
Photo showing a freshly baked rich fruit cake that is my mum’s family Christmas cake recipe
December 23, 2025 at 10:27 PM
Reposted by Gavin Simpson
carlsayer.bsky.social
A mystery seed from a Breckland, eastern England ghostpingo core as found by @hayleymcmechan.bsky.social. help oh botanists!!! 1 mm scale @annasolcova.bsky.social @jo-the-botanist.bsky.social @bramblebotanist.bsky.social @timholtwilson.bsky.social
December 21, 2025 at 7:08 PM
Reposted by Gavin Simpson
physaliacourses.bsky.social
Huge thanks to @gsimpson.bsky.social and everyone who joined our GAMs in R course! 🚀

Happy modeling, everyone! 🎉
December 11, 2025 at 4:08 PM
Reposted by Gavin Simpson
carlsayer.bsky.social
A palaeoecological plant find from a Breckland ghost ice age pond core (East Harling, E. England). Puzzled over by @hayleymcmechan.bsky.social & myself. Help with ID please team!! Seems obvious but stuck. @jo-the-botanist.bsky.social @bramblebotanist.bsky.social @timholtwilson.bsky.social
December 7, 2025 at 6:58 PM
Reposted by Gavin Simpson
theomichelot.bsky.social
We have a preprint about modelling three-dimensional movement tracks, led by @njklappstein.bsky.social.

The model takes the form of a step selection function and, just like in 2D, it can include directional persistence, attraction to targets, and habitat selection.

doi.org/10.1101/2025...
Figure with two panels. Left panel: visualisation of a 3D movement track. Right panel: visualisation of the 3D direction of movement as two angles (one horizontal angle and one vertical angle).
December 2, 2025 at 3:07 PM
Reposted by Gavin Simpson
statsepi.bsky.social
If you want to see me get flustered, but recover to give a banger talk about RSV prophylaxis, the magic of GAMs, and the vital importance of survellence data, then this is the talk for you. With shout-outs for @gsimpson.bsky.social, @vincentab.bsky.social, and @hpscireland.bsky.social
Next Public Health in Action Seminar - Dr Darren Dahly-20251113_130641-Meeting Recording
media.heanet.ie
December 3, 2025 at 8:31 AM
gsimpson.bsky.social
A new release of the mgcv #RStats 📦 is out on CRAN and Simon Wood (U Edinburgh) has added some significant new features despite the small bump in version number:

🌟 scasm() for estimating GAMs with shape constrained smooths. Can be used with any family & smoothness selection is via the EFS method
Model is: b3 <- scasm( y ~ s(x0, bs = "bs", k= k) + s(x1, bs = "sc", xt = "m+", k = k) + s(x2, bs = "bs", k = k) + s(x3, bs = "bs", k = k), family=poisson, bs=200 ) The second smooth `s(x1) is a shape constrained smooth with a positive monotonicity constraint (xt = "m+"). The `bs = 200` arguments uses 200 boostrap samples, which generates bootstrap distributions for each coefficient in the model. These bootstrap samples respect the shape constraints, while the usual +/- 2 SE credible intervals may not. The uncertainty in the partial effects is shown by two credible interval bands; a dark blue central band is a 68% Bayesian credible interval, while the lighter blue outer interval is a 95% Bayesian credible interval. The background of each panel is light grey with white grid lines, in a similar style to ggplot2's default theme.
November 12, 2025 at 11:28 AM
Reposted by Gavin Simpson
resprofnews.bsky.social
Opinion: Chaos is coming for scholarly publishing.

Buckling of commercial models alongside maturing of community-led efforts promises major shifts, says Caroline Edwards (@theblochian.bsky.social).

www.researchprofessionalnews.com/rr-news-uk-v...
Chaos is coming for scholarly publishing - Research Professional News
Buckling of commercial models alongside maturing of community-led efforts promises major shifts, says Caroline Edwards
www.researchprofessionalnews.com
November 12, 2025 at 10:17 AM
Reposted by Gavin Simpson
hadley.nz
filter_out() = yeet()
davisvaughan.bsky.social Davis Vaughan @davisvaughan.bsky.social · Nov 7
We are looking for #rstats community feedback on 3 new dplyr functions!

We're aiming to expand the `filter()` family:

- `filter()` to keep rows
- `filter_out()` to drop rows
- `when_any()` and `when_all()` as modifiers

Read more and leave feedback here:
github.com/tidyverse/ti...
Example of using `filter_out()` on the `penguins` dataset, showing how it is much easier than `filter()`, especially with `NA`s
November 7, 2025 at 4:34 PM
gsimpson.bsky.social
Just updated my manuscript on using #GAMs in #AnimalScience, now on arXiv: doi.org/10.48550/arX...
🐄🐖🪶

Extended examples now show how GAMs go beyond prediction, helping estimate biologically meaningful traits from data.

Code: github.com/gavinsimpson...

🧪 #RStats #mgcv #Statistics #OpenScience
Results of model fitting to the average daily fat content data from @Henderson1990-bd. a) observed average daily fat content (points) and estimated lactation curves from Wood's [-@Wood1967-re] model, a Tweedie GLM, and a Tweedie GAM (lines) with associated 95% confidence (Wood's model) or 95% credible intervals (GLM and GAM). Response residuals for Wood's model (b), Tweedie GLM (c), and Tweedie GAM (d), plus scatter plot smoothers (lines) and 95% credible intervals (shaded ribbons). The fitted lactation curves are like an inverted U, with an extended longer tail to the right (later in lactation). The GAM curve fits the data well, but the fitted curves from Wood's model and the GLM equivalent do not provide good fits to the data, and over predict the amount of fat produced at the peak of lactation, and only grossly capture the decline in fat production later in lactation. The remaining panels show the raw response residuals for the three models, drawing attention to the poor fit; for Wood's model and the GLM there is significant pattern in the residuals, while for the GAM no residual pattern is observed. Quantities of interest derived from Wood's model, a Tweedie GLM, and a Tweedie GAM fitted to the lactation data example: a) the estimated week of peak average daily fat content, b) the estimated average daily fat content at the peak, and c) the rate of change (first derivative) of the lactation curve estimated at a point that is midway between the peak fat content and the end of lacation. The points are the estimated values and the lines are a 95% uncertainty interval. The uncertainty interval is based on the 0.025 and 0.975 percentiles of the bootstrap distribution of model coefficient estimates (Wood's model) or of the posterior distribution (GLM and GAM). Each panel shows three point estimates and an uncertainty range. The three points are the estimates from a GAM, a GLM, and Wood's lactation model. The first panel shows the estimated timing of the peak of lactation, with the GAM capturing the fact that the peak in the data occurs much later in lactation (~ week 11) while the other two models confidently estimate that the peak is in week ~8-9. The GAM estimate has a much wider credible interval, which does include the estimates of Wood's model & the GLM at the extreme end. This reflects the uncertainty in the estimation of the peak timing arising from the data having a wide flat peak. The other panels show the estimates of fat content at the peak, which are broadly similar at ~ 0.7 kg fat per day. The final panel showing the persistency estimate shows the GAM estimate diverging from those of the GLM and Wood's model. Again, the latter two models are overly confident in their estimation of this biologically relevant parameter, despite the fited lactation curve not really following the lactation data. a) Estimated daily growth rate on November 15^th^, 2021 and 95% Bayesian credible interval for the 18 pigs in the pig growth example. b) Posterior distribution of daily growth rate on November 15^th^, 2021, for three pigs (numbers 2, 13, and 17), for whom weight observations ceased before November 1^st^, 2021. In b), the shaded region is the posterior distribution, the point, and thick and thin bars are the posterior median, and 66% and 95% posterior intervals respectively. With the fitted growth curves, we can estimate for any day what the growth rate of each pig was. In this figure I'm showing the estimated growth rate of each pig in the example on November 21st. This growth rate is the first derivative of the fitted growth curve (smooth function). I used posterior sampling to produce the posterior distribution of the growth rate for each pig. These are summarised as a point estimate (median) and ccredible interval in the first panel with most pigs growin at ~1-1.5 kg per day by November 21st, with uncertainties on the order of +/- 0.5 kg per day. The second panel shows the entire posterior distribution of the estimated growth rate for three pigs (2, 13, and 17) for whom there were no weight estimates after November 1st. Here, the model is drawing power from the other pigs to help extrapolate the growth curves for these three pigs, but pig-specific details remain, with the posterior distribution for pig 17 being much more diffuse (wider) than for either pigs 2 or 13, reflecting greater uncertainty for the former animal.
October 29, 2025 at 10:45 AM
Reposted by Gavin Simpson
tah-sci.com
"I don't care for the UK tonight
So stay
Stay"

Fuck you, Katie Lam, absolutely fuck off.

www.youtube.com/watch?v=W38v...
Suede - Black Or Blue (Audio Only)
YouTube video by Suede HQ
www.youtube.com
October 23, 2025 at 6:44 PM
Reposted by Gavin Simpson
betanalpha.bsky.social
I’m seeing some misinformation about pseudo-random number generator best practices going around the internets. Let’s talk about why the pseudo-random number generator seed you use shouldn’t actually have any impact on your results and, consequently, you can choose whatever seed you damn well please.
October 22, 2025 at 7:06 PM
gsimpson.bsky.social
So Framework is supporting projects in the Linux ecosphere that are lead by vile, racist, homophonic, transphobic people and sees nothing wrong with that. And Shopify supports and platforms the racist idiot that built Ruby on Rails, all because their money stream is entirely dependent on Rails
October 23, 2025 at 6:29 AM
gsimpson.bsky.social
Fuck Kramnik
October 22, 2025 at 4:43 PM
Reposted by Gavin Simpson
vincentab.bsky.social
The Pink Book of #MarginalEffects (aka Model to Meaning) ships next week and I've got a backlog of Zoolander memes.

Hope you're hungry for some spam in your timeline.

#RStats #PyData
September 22, 2025 at 4:52 PM
Reposted by Gavin Simpson
vincentab.bsky.social
The new {marginaleffects} release for #RStats (0.30.0) comes with two new vignettes:

1. Speed up computation with automatic differentiation (often 10x gains) marginaleffects.com/bonus/perfor...

2. Power analyses with {marginaleffects} and {DeclareDesign}. marginaleffects.com/bonus/power....
37  Performance – Model to Meaning
marginaleffects.com
September 13, 2025 at 6:37 PM
Reposted by Gavin Simpson
teunbrand.bsky.social
We're glad to finally bring you this update!
thomasp85.com Thomas Lin Pedersen @thomasp85.com · Sep 11
I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN.

It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package.

Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats
ggplot2 4.0.0
A new major version of ggplot2 has been released on CRAN. Find out what is new here.
www.tidyverse.org
September 11, 2025 at 12:02 PM
Reposted by Gavin Simpson
thomasp85.com
I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN.

It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package.

Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats
ggplot2 4.0.0
A new major version of ggplot2 has been released on CRAN. Find out what is new here.
www.tidyverse.org
September 11, 2025 at 11:20 AM
Reposted by Gavin Simpson
physaliacourses.bsky.social
If you’d like to go beyond updates and truly master GAMs in R, join our online course with @gsimpson.bsky.social, 8–11 Dec.

📊 Learn how to fit, interpret, visualise, and communicate GAMs in practice, guided by one of the leading experts in the field.
www.physalia-courses.org/courses-work...
September 2, 2025 at 1:42 PM
gsimpson.bsky.social
🚀 gratia 0.11.1 is out!
Compatibility with ggplot2 4.0.0 + usability improvements + bug fixes.

#Rstats #mgcv #gam #statistics
August 25, 2025 at 12:25 PM
gsimpson.bsky.social
🚀 gratia 0.11.0 is out!

Now has a paper in JOSS — please cite 📄 doi.org/10.21105/jos...

Experimental parallel processing ⚡

New assemble() for building plots 🎨

Better support for complex families + new diagnostics 🧪

Lots of bug fixes + polish ✨

👉 gavinsimpson.github.io/gratia/

#Rstats
An R package for working with generalized additive models
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package.
gavinsimpson.github.io
August 18, 2025 at 6:32 PM
gsimpson.bsky.social
A new visitor 🕷️ to our back yard garden 🪴 Not sure what it is yet, but for scale the abdomen is about as large as the end of a human male’s little finger
A spider, hanging from top to bottom of the image. The abdomen is is patterned with a mix of browns, from cream, through tan, to dark brown A spider, hanging from top to bottom of the image. The abdomen is is patterned with a mix of browns, from cream, through tan, to dark brown
August 18, 2025 at 5:57 PM
Reposted by Gavin Simpson
xkcd.com
Replication Crisis

xkcd.com/3117/
4-panel comic. (1) [Person 1 with ponytail flanked by person with short hair and another person speaking into microphone at podium] PERSON 1: In the early 2010s, researchers found that many major scientific results couldn’t be reproduced. (2) PERSON 1: Over a decade into the replication crisis, we wanted to see if today’s studies have become more robust. (3) PERSON 1: Unfortunately, our replication analysis has found exactly the same problems that those 2010s researchers did. (4) [newspaper with image of speakers from previous panels] Headline: Replication Crisis Solved
July 21, 2025 at 11:54 PM