Generalized Additive Models: An Introduction With R

By Alan Y. Chiang. 2007

8,208 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Visualizing the effects of predictor variables in black box supervised learning models (2016, methodology): “A closely related approach for predictor effect visualization would be to fit a generalized additive model (GAM) (see for example Hastie and Tibshirani (1986) and Wood (2017)) to f.·/, as a surrogate model, and then to plot the individual GAM component functions.”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “For example, prototype vector machines [4], regression models [36, 35], generalized additive models [16, 40, 26, 27], Bayesian case models [19] mind-the-gap models [20], and scoring systems [37] have all been proposed as interpretable models.”
CAM: Causal Additive Models, high-dimensional order search and penalized regression (2013, methodology): “Alternative methods for hypothesis testing in (lowdimensional) additive are possible [43], cf.”
Height–diameter equations for larch plantations in northern and northeastern China: a comparison of the mixed-effects, quantile regression and generalized additive models (2016, background): “GAM is a generalized linear model with a linear predictor involving a sum of smooth functions of covariates (Wood, 2006).”
Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling (2016, methodology): “Next, we used Generalized Additive Models (GAM; Wood, 2006) to estimate each species’ change in association with land cover types (hereafter ‘trajectories’) across seasons – spring, summer and autumn – stratified at a regional scale to account for regional differences in species’ migration…”
Spatially Adaptive Bayesian Penalized Splines With Heteroscedastic Errors (2007, background): “…American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Journal of Computational and Graphical Statistics, Volume 16, Number 2, Pages 265–288 DOI: 10.1198/106186007X208768 265 and Wand 2004; Crainiceanu, Ruppert, and Wand 2005; Wood 2006).”
Local Inference in Additive Models with Decorrelated Local Linear Estimator (2019, methodology): “In the low-dimensional setting, additive models have been carefully investigated from both the methodological and theoretical perspectives [4,20,21,25,27,41].”
Impacts of agricultural intensification on bird communities: New insights from a multi-level and multi-facet approach of biodiversity (2016, methodology): “Then, to determine the importance of fixed effects while limiting uncertainty from model selection, a model averaging procedure was applied (Wood, 2006) (R package {MuMIn}; Barton, 2015) on the models fitted with the Maximum Likelihood method (which allows comparing fixed-effects structure of mixed ”
The role of atmospheric conditions in the seasonal dynamics of North American migration flyways (2014, methodology): “We used generalized additive mixed Journal of Biogeography 41, 1685–1696 ª 2014 John Wiley & Sons Ltd 1688 models (GAMMs) with year as random effect (Wood, 2006) to examine annual trends in average nocturnal wind velocity and bearing within each flyway core component.”
Modeling exposure–lag–response associations with distributed lag non-linear models (2013, neutral): “Also, an extension of DLNMs with penalized splines, characterized by higher flexibility can be explored as well, exploiting previous research on bivariate smoothing techniques [30, 31].”
Bayesian structured additive distributional regression for multivariate responses (2015, methodology): “Applications to illustrate our approach include a joint model of risk factors for chronic and acute childhood undernutrition in India and ecological regressions studying the drivers of election results in Germany.”
Wildfire Prediction to Inform Fire Management: Statistical Science Challenges (2013, methodology): “The binomial probabilities for the nondegenerate component are modeled using penalized spline smoothers (e.g., Wood, 2006) and the mixing probabilities are parameterized to test for shifts away from zero-heavy behavior toward regular or extreme behavior by the multinomial regression of the log-odds…”
Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles (2018, methodology): “The probability of interaction P(Lij = 1) based on a general linear model (GLM) follows a log-linear model of the form (Rohr et al. 2016): logit jiijiij fδ+vδ+)fλ(v=))=(P(L 2 21 (1) where λ, δ1 and δ2 are parameters describing the importance of the "matching" (vi - fj) 2 and of the "centrality" of ”
Accurate intelligible models with pairwise interactions (2013, methodology): “For ANOVA, we use R package mgcv to compute p -values under a Wald test [25].”
Partial migration in expanding red deer populations at northern latitudes – a role for density dependence? (2011, methodology): “We used GAMplot to explore potential nonlinearities (library ‘ mgcv ’ ; Wood 2006).”
The Ratio of Macronutrients, Not Caloric Intake, Dictates Cardiometabolic Health, Aging, and Longevity in Ad Libitum-Fed Mice (2014, methodology): “GAMs were fitted with the help of the mgcv package of the R language (R Core Team, 2012, v.2.15.13) (Wood, 2006).”
Bio‐energetics underpins the spatial response of North Sea plaice (Pleuronectes platessa L.) and sole (Solea solea L.) to climate change (2012, methodology): “A binomial generalized additive model (GAM; Wood, 2006) was used to model the percentage of fish feeding (fish with food in stomach) as a smoother function of Julian day.”
Estimating space‐use and habitat preference from wildlife telemetry data (2008, background): “Although it is possible to select spline flexibility by crossvalidation (Wood 2006), computational restrictions forced us to pre-specify the position and number of knots.”