iiinova — where your digital self keeps evolving

Gavin Simpson @gsimpson.bsky.social · 12/10/2026

If you fit models with glmmTMB(), gratia 📦 has gained support for these model objects, including glmmTMB's nascent capability for fitting penalized spline terms using mgcv's s() You'll need the GitHub versions of both gratia & glmmTMB to try this out #RStats #statistics github.com/glmmTMB/glmm...

Four panels from gratia’s draw() method for a glmmTMB model: a curved smooth effect of x with a shaded confidence band, separate Gaussian QQ plots for site random intercepts and random slopes of z, and the linear parametric effect of z with a shaded confidence band.

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Gavin Simpson @gsimpson.bsky.social · 06/10/2026

📝 My paper on generalized additive models (GAMs) in animal science is out! Learn nonlinear relationships from data, estimate growth rates and compare treatments, with cows, pigs and quail + reproducible #RStats code. Open access: doi.org/10.1016/j.an... #mgcv #AnimalScience #Statistics 🧪

Eight panels show body mass over 78 days for 57 Japanese quail. Columns represent control, T4, T3 and combined T3–T4 hormone treatments; rows separate females and males. Coloured points are measurements, lines are individual growth curves fitted by a hierarchical generalized additive model, and shading shows 95% credible intervals. Growth slows towards a plateau, with females generally reaching higher body masses than males.

Eighteen panels show individual pig growth curves. Black points mark daily average weight estimates from a depth camera; blue lines show curves fitted by a hierarchical generalized additive model, with shaded 95% credible intervals. Weights generally increase over time, with differences in growth and measurement variability among pigs. Uncertainty widens where measurements are sparse or absent.

Four-panel comparison of models fitted to a cow’s average daily milk fat production. Observations rise to a peak around weeks 8–10, then decline. The first panel shows fitted curves and uncertainty bands for Wood’s model, a Tweedie GLM and a Tweedie GAM. Residual plots show curved patterns for Wood’s model and the GLM, but little remaining pattern for the GAM.

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Gavin Simpson @gsimpson.bsky.social · 25/09/2026

96 little pink pigs, 16 pens, 4 rooms 🐷 Our students used LEGO to explore experimental design: randomising treatments, blocking by room, and asking why six pigs sharing a pen don’t give us six independent replicates. Abstract ideas made tangible, one brick at a time #lego #statistics

Three students work around a table on a LEGO model of a pig facility. One places pink pig bricks on yellow pen plates arranged on blue room plates while the others watch. Handouts and laptops sit nearby.

Close-up of a LEGO pig-facility model: four blue room plates each hold four yellow pens with pink pig bricks. Red and green treatment-combination cards cover the four pens in one room, with loose markers beside the model.

Three students work on the LEGO experiment at a classroom table. One adjusts pink pig bricks on the model, another writes on a workshop handout, and a third watches with a notebook open. A large animal skeleton stands in the background.

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Gavin Simpson @gsimpson.bsky.social · 08/09/2026

So I did tell the editor where to shove it — politely of course

Dear Professor Bee,

Thank you for your email, and again for the time that you, the handling editor, and the reviewers have devoted to my manuscript, "Method: Using generalized additive models in the livestock animal sciences". I am very grateful for the constructive review of the paper and was pleased that it was accepted for publication in animal – open space.

I have considered the requested changes to remove first-person formulations such as “I show”, “I describe”, and similar expressions. I respectfully disagree with this stylistic requirement and am not willing to revise the manuscript throughout to adopt an impersonal style. The use of first-person, active prose is a deliberate authorial choice, and there is substantial contemporary guidance encouraging clearer, more direct scientific writing of this kind, including Brennan (2024, *PNAS*, doi:10.1073/pnas.2316966121) and guidance from Nature advocating active rather than passive scientific prose (https://www.nature.com/scitable/topicpage/effective-writing-13815989/).

I would therefore be very happy for the manuscript to remain with animal – open space and proceed to publication, subject to normal copy-editing, provided that the requirement to convert first-person formulations throughout the manuscript to an impersonal style is withdrawn.

If the journal considers this stylistic change to be a condition of publication, please treat this email as my formal request that the manuscript not proceed to publication.

As I signed the publishing agreement following acceptance, I would be grateful if, in that event, you could confirm in writing that the manuscript will not be published by animal – open space and that, pursuant to the “Reversion of Rights” provision in the agreement, all rights granted under the exclusive publishing and distribution licence have reverted to me. I would also appreciate confirmation that I am free to submit the manuscript for consideration by another journal.

I regret that we have reach…

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Gavin Simpson @gsimpson.bsky.social · 17/07/2026

The Lego for an in class experimental design activity just arrived. So I guess I’m done for the day; time to play! The various plates are for blocking structures, and the 1x1 pink blocks are animals ( 🐷 )

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Gavin Simpson @gsimpson.bsky.social · 09/07/2026

This seems intentional, at least according to the documentation positron.posit.co/assistant-co... This should not be the default; the user should have to confirm anything that might send data off their machine Full disclosure: I do have a GitHub education waiver active on my personal GH account.

Screenshot of a GitHub Copilot settings page explaining that code completions appear as ghost text while typing. Under “Enable,” it states that GitHub Copilot completions are enabled by default once signed in to the GitHub Copilot provider, with the phrase “by default” underlined in red (underlining added by the author)

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Gavin Simpson @gsimpson.bsky.social · 23/12/2025

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

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Gavin Simpson @gsimpson.bsky.social · 12/11/2025

🌟 plot.gam() also has a new theme: scheme=2 This draws plots with 68 & 95% intervals (by default) and has a ggplot2-like grey plot background

Example plot showing the partial effects of the estimated smooths in a GAM with terms y ~ x0 + s(x1) + s(x2) + x(x3). Three panels are shown, one per smooth. The first panel is for s(x1) and shows an increasing, slightly curved function that increases slightly more as x1 increases. The second panel shows the partial effects for s(x2), which shows much more variation, being initially negative at x2=0, rising to a peak around x2=0.35, then falling to ~-1 at x2=0.5, then remaining roughly flat followed by a gradual decline thereafter. The final panel shows the partial effect of s(x3), which is ~0 for all x3, with uncertainty band covering 0 too. This predictor is known to have no effect in the simulated data.

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.

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Gavin Simpson @gsimpson.bsky.social · 12/11/2025

🌟 plot.gam() gains a deriv argument, which if TRUE plots derivatives of univariate smooths instead of the usual partial effect plots Partial effect plots can be confusing. With deriv=TRUE you see the change in Y (η) for a small change in X, which is comparable with usual interpretations of model 𝛽

Example plot showing the derivative of the estimated smooths in a GAM with terms y ~ x0 + s(x1) + s(x2) + x(x3). Three panels are shown, one per smooth. The first panel is for s(x1) and shows a positive derivative that increases slightly as x1 increases. The second panel shows the derivative for s(x2), which shows much more variation, being initially strongly positive from x2=0, moving gradually to strongly negative by x2=0.35, then back to 0 at x2=0.5, then remaining roughly flat thereafter. This reflects the strongly peaked nature of the fitted smooth s(x2). The final panel shows the derivative of s(x3), which is ~0 for all x3, with uncertainty band covering 0 too. This predictor is known to have no effect in the simulated data.

The uncertainty in the derivatives 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.

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Gavin Simpson @gsimpson.bsky.social · 12/11/2025

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.

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Gavin Simpson @gsimpson.bsky.social · 29/10/2025

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.

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Gavin Simpson @gsimpson.bsky.social · 18/08/2025

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

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Gavin Simpson @gsimpson.bsky.social · 20/05/2025

Packed house at Åben 🍻 for the second night of #PintOfScience in #Aarhus #Denmark 🧬 🧪 Tonight’s talks; 🎙️ Attack of the Clones - Andrew Ferenbach 🎙️ Is there a Serial Killer Gene? Victoria Twiddy

Panoramic photo of people at Åben, Aarhus, 🇩🇰, for the second night of Pint of Science 2025

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Gavin Simpson @gsimpson.bsky.social · 15/12/2024

Apricot is getting into the festive spirit 😻🎄💡

Photo of a female Devon Rex cat named Apricot sat on a bundle of Christmas tree lights

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Gavin Simpson @gsimpson.bsky.social · 30/11/2024

🚨 New function 🚨 @vincentab.bsky.social's marginaleffects 📦 is great for generating quantities of interest from your model. I've long wanted something similar to plot_predictions() for my gratia 📦 Introducing conditional_values() 🎉 gavinsimpson.github.io/gratia/refer... #RStats #GAMs #mgcv 1/6

The image shows the result of a call to the conditional_values() function where the GAM contains a factor by smooth. In this figure, predicted values from the model conditional on values of x2 are shown for two levels of the factor fac. As we're conditioning on two variables, we include the group means for the 2 levels of fac, plus the smooth for each category. This model also includes a smooth effect of x0. As we don't condition on this covariate, it will be set to a typical representative value (value of observation closest to the median of x0).

The code to produce both images is below. This image is created by the last line of the code.

# packages
library("mgcv")
library("gratia")

# categorical conditions are also handled
df <- data_sim("eg4", seed = 2)
m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = df, method = "REML")
cv <- conditional_values(
m2,
condition = list("fac", x2 = "fivenum")
)
# plot - we see a discrete x axis with five estimates per level of fac
cv |> draw()

# in this example we condition on `x2` and `fac %in% c(2,3)`
cv <- conditional_values(
m2,
condition = list("x2", fac = 2:3)
)
# plot - smooths of `x2` for `fac == 2` and `fac == 3`
cv |> draw()

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Gavin Simpson @gsimpson.bsky.social · 15/11/2024

Peer review fueled by some Åben SIPA at Aarhus Street Food Passing time before I pick up my kids off the train from Copenhagen

Photo shows a manuscript, a green fine liner pen, and a pint of session IPA 🍺

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Gavin Simpson @gsimpson.bsky.social · 28/10/2023

Inbox achievement unlocked 😎

Screenshot from my iPhone showing the GMail icon with 1337 unread messages

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