#mgcvchat — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #mgcvchat, aggregated by home.social.
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made some updates last week to my GAM blog: adaptive smoothing, now with plots of the smoothing parameter function
big thanks to Philip Dixon who asked an interesting question!
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made some updates last week to my GAM blog: adaptive smoothing, now with plots of the smoothing parameter function
big thanks to Philip Dixon who asked an interesting question!
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made some updates last week to my GAM blog: adaptive smoothing, now with plots of the smoothing parameter function
big thanks to Philip Dixon who asked an interesting question!
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made some updates last week to my GAM blog: adaptive smoothing, now with plots of the smoothing parameter function
big thanks to Philip Dixon who asked an interesting question!
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made some updates last week to my GAM blog: adaptive smoothing, now with plots of the smoothing parameter function
big thanks to Philip Dixon who asked an interesting question!
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📈 Yes you can do that in mgcv update
big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html
They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils
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📈 Yes you can do that in mgcv update
big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html
They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils
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📈 Yes you can do that in mgcv update
big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html
They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils
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📈 Yes you can do that in mgcv update
big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html
They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils
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📈 Yes you can do that in mgcv update
big thanks to Zachary Susswein for spotting that my code was out of date in my neighbourhood cross-validation examples: https://calgary.converged.yt/articles/ncv.html https://calgary.converged.yt/articles/ncv_timeseries.html
They are now up-to-date, as is the helper package mgcvUtils: https://github.com/dill/mgcvUtils
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new (out for a while but sitting in my browser from before Christmas) paper in Biometrika from Benjamin Säfken, Thomas Kneib and Simon Wood on smoothing parameter degrees of freedom
Green OA @ Edinburgh https://www.pure.ed.ac.uk/ws/portalfiles/portal/475921820/asae052.pdf
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new (out for a while but sitting in my browser from before Christmas) paper in Biometrika from Benjamin Säfken, Thomas Kneib and Simon Wood on smoothing parameter degrees of freedom
Green OA @ Edinburgh https://www.pure.ed.ac.uk/ws/portalfiles/portal/475921820/asae052.pdf
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new (out for a while but sitting in my browser from before Christmas) paper in Biometrika from Benjamin Säfken, Thomas Kneib and Simon Wood on smoothing parameter degrees of freedom
Green OA @ Edinburgh https://www.pure.ed.ac.uk/ws/portalfiles/portal/475921820/asae052.pdf
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new (out for a while but sitting in my browser from before Christmas) paper in Biometrika from Benjamin Säfken, Thomas Kneib and Simon Wood on smoothing parameter degrees of freedom
Green OA @ Edinburgh https://www.pure.ed.ac.uk/ws/portalfiles/portal/475921820/asae052.pdf
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#mgcv mini-lifehack:
(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.
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#mgcv mini-lifehack:
(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.
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#mgcv mini-lifehack:
(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.
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#mgcv mini-lifehack:
(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.
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#mgcv mini-lifehack:
(assuming you have multithreading enabled) you can get a rough idea of what's happening when fitting a big model by looking at your CPU usage. If only 1 core is being used, the model is still "building" (assembling of design/penalty matrices), once you switch to all cores, then you're actually fitting the model. Sometimes that first model construction phase can take a long time (with a very big model), so it'll probably take a very very long time to fit. So buckle-up.
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oh, hey, I reviewed this! gratia is an excellent tool for mgcv users! Thanks @gavinsimpson!
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oh, hey, I reviewed this! gratia is an excellent tool for mgcv users! Thanks @gavinsimpson!
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oh, hey, I reviewed this! gratia is an excellent tool for mgcv users! Thanks @gavinsimpson!
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oh, hey, I reviewed this! gratia is an excellent tool for mgcv users! Thanks @gavinsimpson!
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oh, hey, I reviewed this! gratia is an excellent tool for mgcv users! Thanks @gavinsimpson!
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Checkout my soundcloud https://arxiv.org/abs/1902.01330 #mgcvchat
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Checkout my soundcloud https://arxiv.org/abs/1902.01330 #mgcvchat
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Checkout my soundcloud https://arxiv.org/abs/1902.01330 #mgcvchat
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Checkout my soundcloud https://arxiv.org/abs/1902.01330 #mgcvchat
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Checkout my soundcloud https://arxiv.org/abs/1902.01330 #mgcvchat
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my mgcv Wrapped 2024
top 5 basis functions:
1. thin-plate regression splines
2. B-splines
3. soap film smoother
4. cubic cyclic splines
5. random effects (psych!) -
my mgcv Wrapped 2024
top 5 basis functions:
1. thin-plate regression splines
2. B-splines
3. soap film smoother
4. cubic cyclic splines
5. random effects (psych!) -
my mgcv Wrapped 2024
top 5 basis functions:
1. thin-plate regression splines
2. B-splines
3. soap film smoother
4. cubic cyclic splines
5. random effects (psych!) -
my mgcv Wrapped 2024
top 5 basis functions:
1. thin-plate regression splines
2. B-splines
3. soap film smoother
4. cubic cyclic splines
5. random effects (psych!) -
my mgcv Wrapped 2024
top 5 basis functions:
1. thin-plate regression splines
2. B-splines
3. soap film smoother
4. cubic cyclic splines
5. random effects (psych!) -
spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.
Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.
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spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.
Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.
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spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.
Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.
-
spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.
Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.
-
spending some more time thinking about neighbourhood cross-validation in #mgcv (see original post here: https://calgary.converged.yt/articles/ncv.html), but for time series.
Pretty nice to be able to get back to a yearly trend here without needing to specify an autoregressive structure. We just need to specify a cross-validation scheme and the autocorrelation is "dealt with" during fitting.
-
I've been writing-up some bits on un/under-documented parts of mgcv. Here's a bit of chat about the new "neighbourhood cross-validation" method that was uploaded to arXiv a wee while ago: https://calgary.converged.yt/articles/ncv.html
More to come on this, including some details on how to setup neighbourhoods in practice.
(Please @ me with errors/typos etc)
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I've been writing-up some bits on un/under-documented parts of mgcv. Here's a bit of chat about the new "neighbourhood cross-validation" method that was uploaded to arXiv a wee while ago: https://calgary.converged.yt/articles/ncv.html
More to come on this, including some details on how to setup neighbourhoods in practice.
(Please @ me with errors/typos etc)
-
I've been writing-up some bits on un/under-documented parts of mgcv. Here's a bit of chat about the new "neighbourhood cross-validation" method that was uploaded to arXiv a wee while ago: https://calgary.converged.yt/articles/ncv.html
More to come on this, including some details on how to setup neighbourhoods in practice.
(Please @ me with errors/typos etc)
-
I've been writing-up some bits on un/under-documented parts of mgcv. Here's a bit of chat about the new "neighbourhood cross-validation" method that was uploaded to arXiv a wee while ago: https://calgary.converged.yt/articles/ncv.html
More to come on this, including some details on how to setup neighbourhoods in practice.
(Please @ me with errors/typos etc)
-
I've been writing-up some bits on un/under-documented parts of mgcv. Here's a bit of chat about the new "neighbourhood cross-validation" method that was uploaded to arXiv a wee while ago: https://calgary.converged.yt/articles/ncv.html
More to come on this, including some details on how to setup neighbourhoods in practice.
(Please @ me with errors/typos etc)
-
Preprint from Simon Wood on the new cross-validation smoothness estimation in #mgcv: https://arxiv.org/abs/2404.16490. It's a neat performant + data-efficient way to estimate GAMs based on complex CV splits (like spatial/temporal/phylo ones).
See ?NCV in latest {mgcv} for examples (https://cran.r-universe.dev/mgcv/doc/manual.html#NCV)
I might write a helper to convert {rsample}/{spatialsample} objects into mgcv's funny CV indexing structure.
#rstats #ml #tidymodels #mgcvchat @MikeMahoney218 @gavinsimpson @ericJpedersen @millerdl
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Preprint from Simon Wood on the new cross-validation smoothness estimation in #mgcv: https://arxiv.org/abs/2404.16490. It's a neat performant + data-efficient way to estimate GAMs based on complex CV splits (like spatial/temporal/phylo ones).
See ?NCV in latest {mgcv} for examples (https://cran.r-universe.dev/mgcv/doc/manual.html#NCV)
I might write a helper to convert {rsample}/{spatialsample} objects into mgcv's funny CV indexing structure.
#rstats #ml #tidymodels #mgcvchat @MikeMahoney218 @gavinsimpson @ericJpedersen @millerdl
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Preprint from Simon Wood on the new cross-validation smoothness estimation in #mgcv: https://arxiv.org/abs/2404.16490. It's a neat performant + data-efficient way to estimate GAMs based on complex CV splits (like spatial/temporal/phylo ones).
See ?NCV in latest {mgcv} for examples (https://cran.r-universe.dev/mgcv/doc/manual.html#NCV)
I might write a helper to convert {rsample}/{spatialsample} objects into mgcv's funny CV indexing structure.
#rstats #ml #tidymodels #mgcvchat @MikeMahoney218 @gavinsimpson @ericJpedersen @millerdl
-
Preprint from Simon Wood on the new cross-validation smoothness estimation in #mgcv: https://arxiv.org/abs/2404.16490. It's a neat performant + data-efficient way to estimate GAMs based on complex CV splits (like spatial/temporal/phylo ones).
See ?NCV in latest {mgcv} for examples (https://cran.r-universe.dev/mgcv/doc/manual.html#NCV)
I might write a helper to convert {rsample}/{spatialsample} objects into mgcv's funny CV indexing structure.
#rstats #ml #tidymodels #mgcvchat @MikeMahoney218 @gavinsimpson @ericJpedersen @millerdl
-
Preprint from Simon Wood on the new cross-validation smoothness estimation in #mgcv: https://arxiv.org/abs/2404.16490. It's a neat performant + data-efficient way to estimate GAMs based on complex CV splits (like spatial/temporal/phylo ones).
See ?NCV in latest {mgcv} for examples (https://cran.r-universe.dev/mgcv/doc/manual.html#NCV)
I might write a helper to convert {rsample}/{spatialsample} objects into mgcv's funny CV indexing structure.
#rstats #ml #tidymodels #mgcvchat @MikeMahoney218 @gavinsimpson @ericJpedersen @millerdl
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Treat today at Edinburgh Uni stats seminar: Emiko Dupont (Bath) talking about her new work on spatial confounding (https://arxiv.org/abs/2309.16861) #statschat #statistics #mgcvchat