Cited by 13,852Statistical Methods and InferenceStatistical Methods and ApplicationsStatistical Methods and Bayesian Inference

The DOI matches its Crossref record.

Generalized Additive Models: An Introduction with R

Wood, Simon N.·2017·Chapman & Hall/CRC eBooks

DOI 10.1201/9781315370279

Open this paper in CoChat — free account

Abstract

The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.

Integrity signals

Registry

Crossref · 10.1201/9781315370279

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Sorted by citations

Items 101–200 of 1,064

Sustaining recreational quality of European lakes: minimizing the health risks from algal blooms through phosphorus control
Carvalho, L., McDonald, C., de Hoyos, C., Mischke, U., Phillips, G. L., Borics, G., Poikāne, S., Skjelbred, B., Solheim, A. L., Wichelen, J. V., et al. (2013) · Journal of Applied Ecology · doi:10.1111/1365-2664.12059 · Cited by 216
Monitoring Great Ape and Elephant Abundance at Large Spatial Scales: Measuring Effectiveness of a Conservation Landscape
Stokes, E. J., Strindberg, S., Bakabana, P. C., Elkan, P. W., Iyenguet, F. C., Madzoké, B., Malanda, G. A. F., Mowawa, B. S., Moukoumbou, C., Ouakabadio, F. K., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010294 · Cited by 216
A continental system for forecasting bird migration
Doren, B. M. V., & Horton, K. G. (2018) · Science · doi:10.1126/science.aat7526 · Cited by 215
Performance of large-scale irrigation projects in sub-Saharan Africa
Higginbottom, T. P., Adhikari, R., Dimova, R. D., Redicker, S., & Foster, T. (2021) · Nature Sustainability · doi:10.1038/s41893-020-00670-7 · Cited by 213
Mediterranean fishery discards: review of the existing knowledge
Tsagarakis, K., Palialexis, A., & Vassilopoulou, V. (2013) · ICES Journal of Marine Science · doi:10.1093/icesjms/fst074 · Cited by 212
sdmTMB : An R Package for Fast, Flexible, and User-Friendly Generalized Linear Mixed Effects Models with Spatial and Spatiotemporal Random Fields
Anderson, S. C., Ward, E. J., English, P. A., Barnett, L. A. K., & Thorson, J. T. (2025) · Journal of Statistical Software · doi:10.18637/jss.v115.i02 · Cited by 218
Effects of diurnal temperature range on cardiovascular and respiratory hospital admissions in Korea
Lim, Y., Hong, Y., & Kim, H. (2012) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2011.12.048 · Cited by 202
Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within E urope
Morellet, N., Bonenfant, C., Börger, L., Ossi, F., Cagnacci, F., Heurich, M., Kjellander, P., Linnell, J. D. C., Nicoloso, S., Šustr, P., et al. (2013) · Journal of Animal Ecology · doi:10.1111/1365-2656.12105 · Cited by 221
Decadal-Scale Changes of Dinoflagellates and Diatoms in the Anomalous Baltic Sea Spring Bloom
Klais, R., Tamminen, T., Kremp, A., Spilling, K., & Olli, K. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021567 · Cited by 213
Three points to consider when choosing a LM or GLM test for count data
Warton, D. I., Lyons, M., Stoklosa, J., & Ives, A. R. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12552 · Cited by 192
Carotenoid Pigmentation in Antarctic Heterotrophic Bacteria as a Strategy to Withstand Environmental Stresses
Dieser, M., Greenwood, M., & Foreman, C. M. (2010) · Arctic Antarctic and Alpine Research · doi:10.1657/1938-4246-42.4.396 · Cited by 197
Intrinsic activity development unfolds along a sensorimotor–association cortical axis in youth
Sydnor, V. J., Larsen, B., Seidlitz, J., Adebimpe, A., Alexander‐Bloch, A. F., Bassett, D. S., Bertolero, M. A., Cieslak, M., Covitz, S. C., Yong Fan, A., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01282-y · Cited by 225
Habitat loss over six decades accelerates regional and local biodiversity loss via changing landscape connectance
Horváth, Z., Ptáčník, R., Vad, C. F., & Chase, J. M. (2019) · Ecology Letters · doi:10.1111/ele.13260 · Cited by 192
Global habitat preferences of commercially valuable tuna
Arrizabalaga, H., Dufour, F., Kell, L., Merino, G., Ibaibarriaga, L., Chust, G., Irigoien, X., Santiago, J., Murua, H., Fraile, I., et al. (2015) · Deep Sea Research Part II: Topical Studies in Oceanography · doi:10.1016/j.dsr2.2014.07.001 · Cited by 194
Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke
Abraham, G., Malik, R., Yonova‐Doing, E., Salim, A., Wang, T., Danesh, J., Butterworth, A. S., Howson, J. M. M., Inouye, M., & Dichgans, M. (2019) · Nature Communications · doi:10.1038/s41467-019-13848-1 · Cited by 191
Estimated acute effects of ambient ozone and nitrogen dioxide on mortality in the Pearl River Delta of Southern China
Tao, Y., Huang, W., Huang, X., Zhong, L., Lu, S.-E., Li, Y., Dai, L., Zhang, Y., & Zhu, T. (2012) · Environmental Health Perspectives · doi:10.1289/ehp.1103715 · Cited by 190
Demythologizing the word frequency effect
Baayen, R. H. (2010) · The Mental Lexicon · doi:10.1075/ml.5.3.10baa · Cited by 185
Forest fragments modulate the provision of multiple ecosystem services
Mitchell, M. G. E., Bennett, E. M., & Gonzalez, A. (2014) · Journal of Applied Ecology · doi:10.1111/1365-2664.12241 · Cited by 198
Frequency in lexical processing
Baayen, R. H., Milin, P., & Ramscar, M. (2016) · Aphasiology · doi:10.1080/02687038.2016.1147767 · Cited by 185
Statistical Methodology in Studies of Prenatal Exposure to Mixtures of Endocrine-Disrupting Chemicals: A Review of Existing Approaches and New Alternatives
Lazarevic, N., Barnett, A. G., Sly, P. D., & Knibbs, L. D. (2019) · Environmental Health Perspectives · doi:10.1289/ehp2207 · Cited by 192
Unfold: an integrated toolbox for overlap correction, non-linear modeling, and regression-based EEG analysis
Ehinger, B., & Dimigen, O. (2019) · PeerJ · doi:10.7717/peerj.7838 · Cited by 181
Detecting long‐term growth trends using tree rings: a critical evaluation of methods
Peters, R. L., Groenendijk, P., Vlam, M., & Zuidema, P. A. (2014) · Global Change Biology · doi:10.1111/gcb.12826 · Cited by 180
Higher levels and blunted diurnal variation of cortisol in frail older women
Varadhan, R., Walston, J., Cappola, A. R., Carlson, M. C., Wand, G. S., & Fried, L. P. (2008) · The Journals of Gerontology Series A: Biological Sciences and Medical Sciences · doi:10.1093/gerona/63.2.190 · Cited by 182
Outbreaks of Vector-Borne and Zoonotic Diseases Are Associated With Changes in Forest Cover and Oil Palm Expansion at Global Scale
Morand, S., & Lajaunie, C. (2021) · Frontiers in Veterinary Science · doi:10.3389/fvets.2021.661063 · Cited by 213
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Jiang, S., Sweet, L., Blougouras, G., Brenning, A., Li, W., Reichstein, M., Denzler, J., Shangguan, W., Yu, G., Huang, F., et al. (2024) · Earth s Future · doi:10.1029/2024ef004540 · Cited by 209
Plant species richness increases with light availability, but not variability, in temperate forests understorey
Dormann, C. F., Bagnara, M., Boch, S., Hinderling, J., Janeiro-Otero, A., Schäfer, D., Schall, P., & Hartig, F. (2020) · BMC Ecology · doi:10.1186/s12898-020-00311-9 · Cited by 180
Our footprint on Antarctica competes with nature for rare ice-free land
Brooks, S. T., Jabour, J., van den Hoff, J., & Bergstrom, D. M. (2019) · Nature Sustainability · doi:10.1038/s41893-019-0237-y · Cited by 178
Climate change, biological invasions, and the shifting distribution of Mediterranean fishes: A large‐scale survey based on local ecological knowledge
Azzurro, E., Sbragaglia, V., Cerri, J., Bariche, M., Bolognini, L., Ben Souissi, J., Busoni, G., Coco, S., Chryssanthi, A., Fanelli, E., et al. (2019) · Global Change Biology · doi:10.1111/gcb.14670 · Cited by 179
Large-scale distribution of tuna species in a warming ocean
Erauskin‐Extramiana, M., Arrizabalaga, H., Hobday, A. J., Cabré, A., Ibaibarriaga, L., Arregui, I., Murua, H., & Chust, G. (2019) · Global Change Biology · doi:10.1111/gcb.14630 · Cited by 176
The geography of climate and the global patterns of species diversity
Coelho, M. T. P., Barreto, E., Rangel, T. F., Diniz‐Filho, J. A. F., Wüest, R. O., Bach, W., Skeels, A., McFadden, I. R., Roberts, D. W., Pellissier, L., et al. (2023) · Nature · doi:10.1038/s41586-023-06577-5 · Cited by 191
ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures
Merder, J., Freund, J. A., Feudel, U., Hansen, C. T., Hawkes, J. A., Jacob, B., Klaproth, K., Niggemann, J., Noriega‐Ortega, B. E., Osterholz, H., et al. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.9b05659 · Cited by 173
Trends and drivers of marine debris on the Atlantic coast of the United States 1997–2007
Ribic, C. A., Sheavly, S. B., Rugg, D. J., & Erdmann, E. S. (2010) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2010.03.021 · Cited by 172
Discrimination in lexical decision
Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., & Baayen, R. H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171935 · Cited by 182
Scalable Visualization Methods for Modern Generalized Additive Models
Fasiolo, M., Nedellec, R., Goude, Y., & Wood, S. N. (2019) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2019.1629942 · Cited by 171
Self-citation
Passive Stiffness of Myocardium From Congenital Heart Disease and Implications for Diastole
Chaturvedi, R. R., Herron, T., Simmons, R., Shore, D., Kumar, P., Sethia, B., Chua, F., Vassiliadis, E., & Kentish, J. C. (2010) · Circulation · doi:10.1161/circulationaha.109.850677 · Cited by 167
Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species
Abrahms, B. L., Welch, H., Brodie, S. J., Jacox, M. G., Becker, E. A., Bograd, S. J., Irvine, L. M., Palacios, D. M., Mate, B. R., & Hazen, E. L. (2019) · Diversity and Distributions · doi:10.1111/ddi.12940 · Cited by 167
Fine-scale prey aggregations and foraging ecology of humpback whales Megaptera novaeangliae
Hazen, E. L., Friedlaender, A., Thompson, M., Ware, C., Weinrich, M. T., Halpin, P., & Wiley, D. N. (2009) · Marine Ecology Progress Series · doi:10.3354/meps08108 · Cited by 165
Multiple anthropogenic pressures eliminate the effects of soil microbial diversity on ecosystem functions in experimental microcosms
Yang, G., Ryo, M., Roy, J., Lammel, D. R., Ballhausen, M., Jing, X., Zhu, X., & Rillig, M. C. (2022) · Nature Communications · doi:10.1038/s41467-022-31936-7 · Cited by 168
Predicting dispersal distance in mammals: a trait‐based approach
Whitmee, S., & Orme, C. D. L. (2012) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2012.02030.x · Cited by 168
Long‐term phenological trends, species accumulation rates, aphid traits and climate: five decades of change in migrating aphids
Bell, J. R., Alderson, L. J., Izera, D., Kruger, T., Parker, S., Pickup, J., Shortall, C. R., Taylor, M. S., VERRIER, P. J., & Harrington, R. (2014) · Journal of Animal Ecology · doi:10.1111/1365-2656.12282 · Cited by 178
Associations between perfluoroalkyl substances and serum lipids in a Swedish adult population with contaminated drinking water
Li, Y., Barregård, L., Xu, Y., Scott, K., Pineda, D., Lindh, C. H., Jakobsson, K., & Fletcher, T. (2020) · Environmental Health · doi:10.1186/s12940-020-00588-9 · Cited by 172
Cellular correlates of cortical thinning throughout the lifespan
Vidal-Pineiro, D., Parker, N., Shin, J., French, L., Grydeland, H., Jackowski, A. P., Mowinckel, A. M., Patel, Y., Pausova, Z., Salum, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78471-3 · Cited by 154
CHANGES IN CRIMINAL OFFENDING AROUND THE TIME OF JOB ENTRY: A STUDY OF EMPLOYMENT AND DESISTANCE
Skarðhamar, T., & Savolainen, J. (2014) · Criminology · doi:10.1111/1745-9125.12037 · Cited by 165
A simple function for full‐subsets multiple regression in ecology with R
Fisher, R., Wilson, S. K., Sin, T. M., Lee, A. C., & Langlois, T. J. (2018) · Ecology and Evolution · doi:10.1002/ece3.4134 · Cited by 160
A Review of Software for Spatial Econometrics in R
Bivand, R., Millo, G., & Piras, G. (2021) · Mathematics · doi:10.3390/math9111276 · Cited by 187
Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis
Vannan, A., Lyu, R., Williams-Katek, A. L., Negretti, N. M., Mee, E. D., Hirsh, J., Hirsh, S., Hadad, N., Nichols, D. S., Calvi, C. L., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02080-x · Cited by 195
Quantitative approaches in climate change ecology
Brown, C. J., Schoeman, D. S., Sydeman, W. J., Brander, K., Buckley, L. B., Burrows, M. T., Duarte, C. M., Moore, P. J., Pandolfi, J. M., Poloczanska, E. S., et al. (2011) · Global Change Biology · doi:10.1111/j.1365-2486.2011.02531.x · Cited by 167
Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology
Gollob, C., Ritter, T., & Nothdurft, A. (2020) · Remote Sensing · doi:10.3390/rs12091509 · Cited by 162
Sleep and longitudinal cognitive performance in preclinical and early symptomatic Alzheimer’s disease
Lucey, B. P., Wisch, J. K., Boerwinkle, A. H., Landsness, E. C., Toedebusch, C. D., McLeland, J., Butt, O. H., Hassenstab, J. J., Morris, J. C., Ances, B. M., et al. (2021) · Brain · doi:10.1093/brain/awab272 · Cited by 162
Wind turbines cause functional habitat loss for migratory soaring birds
Marques, A. T., Santos, C. D., Hanssen, F., et al. (2019) · Journal of Animal Ecology · doi:10.1111/1365-2656.12961 · Cited by 161
Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis
Aburto, J. M., Kashyap, R., Schöley, J., Angus, C., Ermisch, J., Mills, M. C., & Dowd, J. B. (2021) · Journal of Epidemiology and Community Health · doi:10.1136/jech-2020-215505 · Cited by 159
The impact of COVID-19 restriction measures on loneliness among older adults in Austria
Stolz, E., Mayerl, H., & Freidl, W. (2020) · European Journal of Public Health · doi:10.1093/eurpub/ckaa238 · Cited by 168
Global patterns and drivers of tree diversity integrated across a continuum of spatial grains
Keil, P., & Chase, J. M. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0799-0 · Cited by 161
Changes in social contacts in England during the COVID-19 pandemic between March 2020 and March 2021 as measured by the CoMix survey: A repeated cross-sectional study
Gimma, A., Munday, J. D., Wong, K. L., Coletti, P., van Zandvoort, K., Prem, K., CMMID COVID-19 working group, Klepac, P., Rubin, G. J., Funk, S., et al. (2022) · PLoS Medicine · doi:10.1371/journal.pmed.1003907 · Cited by 150
Air quality and social deprivation in four French metropolitan areas—A localized spatio-temporal environmental inequality analysis
Padilla, C. M., Kihal-Talantikite, W., Vieira, V. M., et al. (2014) · Environmental Research · doi:10.1016/j.envres.2014.07.017 · Cited by 156
GAMLSS: A distributional regression approach
Stasinopoulos, M. D., Rigby, R. A., & Bastiani, F. D. (2018) · Statistical Modelling · doi:10.1177/1471082x18759144 · Cited by 156
Neuroanatomical changes observed over the course of a human pregnancy
Pritschet, L., Taylor, C. M., Cossio, D. M., Faskowitz, J., Santander, T., Handwerker, D. A., Grotzinger, H., Layher, E., Chrastil, E. R., & Jacobs, E. G. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01741-0 · Cited by 194
Fast Calibrated Additive Quantile Regression
Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1725521 · Cited by 195
Self-citation
Profiling global cancer incidence and mortality by socioeconomic development
Lortet‐Tieulent, J., Georges, D., Bray, F., & Vaccarella, S. (2020) · International Journal of Cancer · doi:10.1002/ijc.33114 · Cited by 154
Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation
Delisle, Z. J., Flaherty, E. A., Nobbe, M. R., Wzientek, C. M., & Swihart, R. K. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.617996 · Cited by 160
There Is No Evidence That Associations Between Adolescents’ Digital Technology Engagement and Mental Health Problems Have Increased
Vuorre, M., Orben, A., & Przybylski, A. K. (2021) · Clinical Psychological Science · doi:10.1177/2167702621994549 · Cited by 158
Climatic constraints on trait‐based forest assembly
Laughlin, D. C., Fulé, P. Z., Huffman, D. W., Crouse, J. E., & Laliberté, É. (2011) · Journal of Ecology · doi:10.1111/j.1365-2745.2011.01885.x · Cited by 152
Defining and predicting heat waves in Bangladesh
Nissan, H., Burkart, K., Coughlan de Perez, E., Van Aalst, M., & Mason, S. (2017) · Journal of Applied Meteorology and Climatology · doi:10.1175/jamc-d-17-0035.1 · Cited by 145
Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities—a case study in Sheffield
Munir, S., Mayfield, M., Coca, D., Jubb, S. A., & Osammor, O. (2019) · Environmental Monitoring and Assessment · doi:10.1007/s10661-019-7231-8 · Cited by 149
Habitat characteristics of skipjack tuna ( Katsuwonus pelamis ) in the western North Pacific: a remote sensing perspective
MUGO, R., SAITOH, S., NIHIRA, A., & KUROYAMA, T. (2010) · Fisheries Oceanography · doi:10.1111/j.1365-2419.2010.00552.x · Cited by 149
What we (don't) know about global plant diversity
Cornwell, W. K., Pearse, W. D., Dalrymple, R. L., & Zanne, A. E. (2019) · Ecography · doi:10.1111/ecog.04481 · Cited by 145
Interventions for avian influenza A (H5N1) risk management in live bird market networks
Fournié, G., Guitián, J., Desvaux, S., Cương, V. C., Dung, D. H., Pfeiffer, D. U., Mangtani, P., & Ghani, A. C. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220815110 · Cited by 146
The influence of time, soil characteristics, and land‐use history on soil phosphorus legacies: a global meta‐analysis
MacDonald, G. K., Bennett, E. M., & Taranu, Z. E. (2012) · Global Change Biology · doi:10.1111/j.1365-2486.2012.02653.x · Cited by 148
The ecological impact of city lighting scenarios: exploring gap crossing thresholds for urban bats
Hale, J., Fairbrass, A. J., Matthews, T. J., Davies, G., & Sadler, J. P. (2015) · Global Change Biology · doi:10.1111/gcb.12884 · Cited by 144
Benthic composition changes on coral reefs at global scales
Tebbett, S. B., Connolly, S. R., & Bellwood, D. R. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01937-2 · Cited by 161
A Sensorimotor Role for Traveling Waves in Primate Visual Cortex
Zanos, T. P., Mineault, P., Nasiotis, K., Guitton, D., & Pack, C. C. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.043 · Cited by 139
Relationships of total phosphorus and chlorophyll in lakes worldwide
Quinlan, R., Filazzola, A., Mahdiyan, O., Shuvo, A., Blagrave, K., Ewins, C., Moslenko, L., Gray, D. K., O’Reilly, C. M., & Sharma, S. (2020) · Limnology and Oceanography · doi:10.1002/lno.11611 · Cited by 146
Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data
Steen, V. A., Tingley, M. W., Paton, P. W. C., et al. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13525 · Cited by 143
Fire hazard modulation by long-term dynamics in land cover and dominant forest type in eastern and central Europe
Feurdean, A., Vannière, B., Finsinger, W., Warren, D. L., Connor, S., Forrest, M., Liakka, J., Panait, A. M., Werner, C., Andrič, M., et al. (2020) · Biogeosciences · doi:10.5194/bg-17-1213-2020 · Cited by 148
Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model?
Gomes, D. G. E. (2022) · PeerJ · doi:10.7717/peerj.12794 · Cited by 169
Food-web inferences of stable isotope spatial patterns in copepods and yellowfin tuna in the pelagic eastern Pacific Ocean
Olson, R. J., Popp, B., Graham, B., López-Ibarra, G. A., Galván-Magaña, F., Lennert-Cody, C., Bocanegra-Castillo, N., Wallsgrove, N., Gier, E., Alatorre-Ramírez, V., et al. (2010) · Progress In Oceanography · doi:10.1016/j.pocean.2010.04.026 · Cited by 3
Catchment properties and the photosynthetic trait composition of freshwater plant communities
Iversen, L. L., Winkel, A., Baastrup‐Spohr, L., Hinke, A. B., Alahuhta, J., Baattrup‐Pedersen, A., Birk, S., Brodersen, P., Chambers, P. A., Ecke, F., et al. (2019) · Science · doi:10.1126/science.aay5945 · Cited by 139
Association of Structural Magnetic Resonance Imaging Measures With Psychosis Onset in Individuals at Clinical High Risk for Developing Psychosis: An ENIGMA Working Group Mega-analysis
ENIGMA Clinical High Risk for Psychosis Working Group, Jalbrzikowski, M., Hayes, R. A., Wood, S. J., Nordholm, D., Zhou, J. H., Fusar-Poli, P., Uhlhaas, P. J., Takahashi, T., Sugranyes, G., et al. (2021) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2021.0638 · Cited by 137
Self-citation
Empirical estimate of forestation-induced precipitation changes in Europe
Meier, R., Schwaab, J., Seneviratne, S. I., Sprenger, M. A., Lewis, E., & Davin, É. L. (2021) · Nature Geoscience · doi:10.1038/s41561-021-00773-6 · Cited by 148
Living standards and plague in London, 1560–1665
Cummins, N., Kelly, M., & Ó Gráda, C. (2016) · The Economic History Review · doi:10.1111/ehr.12098 · Cited by 139
Generalized Additive Models Used to Predict Species Abundance in the Gulf of Mexico: An Ecosystem Modeling Tool
Drexler, M., & Ainsworth, C. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064458 · Cited by 137
Penalized function-on-function regression
Ivanescu, A. E., Staicu, A., Scheipl, F., & Greven, S. (2014) · Computational Statistics · doi:10.1007/s00180-014-0548-4 · Cited by 130
Estimating global chlorophyll changes over the past century
Boyce, D. G., Dowd, M. G., Lewis, M. R., & Worm, B. (2014) · Progress In Oceanography · doi:10.1016/j.pocean.2014.01.004 · Cited by 147
STRengthening Analytical Thinking for Observational Studies: the STRATOS initiative
Sauerbrei, W., Abrahamowicz, M., Altman, D. G., le Cessie, S., Carpenter, J. R., & initiative, O. B. O. T. S. (2014) · Statistics in Medicine · doi:10.1002/sim.6265 · Cited by 143
Gendered citation practices in the field of communication
Wang, X., Dworkin, J. D., Zhou, D., Stiso, J., Falk, E. B., Bassett, D. S., Zurn, P., & Lydon‐Staley, D. M. (2021) · Annals of the International Communication Association · doi:10.1080/23808985.2021.1960180 · Cited by 156
Elbow and wrist/hand symptoms among 6,943 computer operators: A 1‐year follow‐up study (the NUDATA study)
Lassen, C. F., Mikkelsen, S., Kryger, A. I., et al. (2004) · American Journal of Industrial Medicine · doi:10.1002/ajim.20081 · Cited by 132
Hypometabolism and basking: the strategies of Alpine ibex to endure harsh over-wintering conditions
Signer, C., Ruf, T., & Arnold, W. (2011) · Functional Ecology · doi:10.1111/j.1365-2435.2010.01806.x · Cited by 131
Movements and foraging of predators associated with mesophotic coral reefs and their potential for linking ecological habitats
Papastamatiou, Y. P., Meyer, C., Kosaki, R. K., et al. (2014) · Marine Ecology Progress Series · doi:10.3354/meps11110 · Cited by 130
Constant mortality and fertility over age in Hydra
Schaible, R., Scheuerlein, A., Dańko, M. J., Gampe, J., Martínez, D. E., & Vaupel, J. W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1521002112 · Cited by 127
Inference and computation with generalized additive models and their extensions
Wood, S. N. (2020) · Test · doi:10.1007/s11749-020-00711-5 · Cited by 142
Self-citation
Drought legacies are short, prevail in dry conifer forests and depend on growth variability
Gazol, A., Camarero, J. J., Sánchez‐Salguero, R., Vicente-Serrano, S. M., Serra‐Maluquer, X., Gutiérrez, E., de Luis, M., Sangüesa‐Barreda, G., Novak, K., Rozas, V., et al. (2020) · Journal of Ecology · doi:10.1111/1365-2745.13435 · Cited by 129
Population Densities, Vegetation Green-Up, and Plant Productivity: Impacts on Reproductive Success and Juvenile Body Mass in Reindeer
Tveraa, T., Stien, A., Bårdsen, B., & Fauchald, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0056450 · Cited by 129
Racial, Ethnic, and Socioeconomic Disparities in Multiple Measures of Blue and Green Spaces in the United States
Klompmaker, J. O., Hart, J. E., Bailey, C. R., Browning, M. H. E. M., Casey, J. A., Hanley, J. R., Minson, C. T., Ogletree, S. S., Rigolon, A., Laden, F., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp11164 · Cited by 143
The Seascape of Demersal Fish Nursery Areas in the North Mediterranean Sea, a First Step Towards the Implementation of Spatial Planning for Trawl Fisheries
Colloca, F., Garofalo, G., Bitetto, I., Facchini, M. T., Grati, F., Martiradonna, A., Mastrantonio, G., Nikolioudakis, N., Ordines, F., Scarcella, G., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119590 · Cited by 125
Productive instability of coral reef fisheries after climate-driven regime shifts
Robinson, J. P. W., Wilson, S. K., Robinson, J., Gerry, C., Lucas, J., Assan, C., Govinden, R., Jennings, S., & Graham, N. A. J. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0715-z · Cited by 132
Past and future decline of tropical pelagic biodiversity
Yasuhara, M., Wei, C., Kučera, M., Costello, M. J., Tittensor, D. P., Kiessling, W., Bonebrake, T. C., Tabor, C., Feng, R., Baselga, A., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916923117 · Cited by 131
Regression-based analysis of combined EEG and eye-tracking data: Theory and applications
Dimigen, O., & Ehinger, B. (2021) · Journal of Vision · doi:10.1167/jov.21.1.3 · Cited by 123
Phenological responses to multiple environmental drivers under climate change: insights from a long‐term observational study and a manipulative field experiment
Wadgymar, S. M., Ogilvie, J. E., Inouye, D. W., Weis, A. E., & Anderson, J. T. (2018) · New Phytologist · doi:10.1111/nph.15029 · Cited by 129
Holocene El Niño–Southern Oscillation variability reflected in subtropical Australian precipitation
Barr, C., Tibby, J., Leng, M. J., Tyler, J. J., Henderson, A. C. G., Overpeck, J. T., Simpson, G. L., Cole, J. E., Phipps, S. J., Marshall, J. C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-38626-3 · Cited by 127
Survey completeness of a global citizen‐science database of bird occurrence
Sorte, F. A. L., & Somveille, M. (2019) · Ecography · doi:10.1111/ecog.04632 · Cited by 128

Authors: Wood, S. N.
Corresponding author: Wood, Simon N.
Venue: Chapman & Hall/CRC eBooks
Publisher: Chapman and Hall/CRC
Published: 2017-05-18
DOI: 10.1201/9781315370279
OpenAlex: W3112717819
Cited by: 13,852
References: 0
Citations by year: 2026: 1378 · 2025: 1355 · 2024: 1085 · 2023: 934 · 2022: 976 · 2021: 1373 · 2020: 1048 · 2019: 787 · 2018: 603 · 2017: 556
FWCI: 208.09
Type: book
Language: en
Open access: closed
Locations: Chapman & Hall/CRC eBooks (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords: generalized additive models, penalized spline regression, linear mixed models, generalized linear models, linear models, R software, nonparametric regression

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Generalized Additive Models: An Introduction with R

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cited by

Related

Metadata

Record

About these checks