Gavin Simpson

@gsimpson.bsky.social

2791 Followers 746 Following

(Palaeo)[ecologist | limnologist] & #fakeStatistican, #rstats user, wielder of #GAMs. He/him/his. Opinions mine…

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)
Photo showing a freshly baked rich fruit cake that is my mum’s family Christmas cake recipe
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.
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.
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.
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.
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
Panoramic photo of people at Åben, Aarhus, 🇩🇰, for the second night of Pint of Science 2025
Photo of a female Devon Rex cat named Apricot sat on a bundle of Christmas tree lights
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()
Photo shows a manuscript, a green fine liner pen, and a pint of session IPA 🍺
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