The DOI matches its Crossref record.
Generalized Additive Models: An Introduction with R
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“evidence suggesting that MBIs, compared to active controls, were more effective in enhancing state and trait attentional skills [91], executive functioning [92], and emotional clarity [93].”
Citations
Cited by
Sorted by citations
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998
A protocol for data exploration to avoid common statistical problems: Data exploration
Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00001.x · Cited by 8,228
A general approach to causal mediation analysis.
Imai, K., Keele, L., & Tingley, D. (2010) · Psychological Methods · doi:10.1037/a0020761 · Cited by 4,143
F. Dormann, C., M. McPherson, J., B. Araújo, M., Bivand, R., Bolliger, J., Carl, G., G. Davies, R., Hirzel, A., Jetz, W., Daniel Kissling, W., et al. (2007) · Ecography · doi:10.1111/j.2007.0906-7590.05171.x · Cited by 3,433
Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases
Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., Massari, M., Salmaso, S., Tomba, G. S., Wallinga, J., et al. (2008) · PLoS Medicine · doi:10.1371/journal.pmed.0050074 · Cited by 2,900
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583
Distance software: design and analysis of distance sampling surveys for estimating population size
Thomas, L., Buckland, S. T., Rexstad, E. A., Laake, J. L., Strindberg, S., Hedley, S. L., Bishop, J. R. B., Marques, T. A., & Burnham, K. P. (2009) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2009.01737.x · Cited by 2,183
Decline of the North American avifauna
Rosenberg, K. V., Dokter, A. M., Blancher, P. J., Sauer, J. R., Smith, A. C., Smith, P. A., Stanton, J. C., Panjabi, A. O., Helft, L., Parr, M. J., et al. (2019) · Science · doi:10.1126/science.aaw1313 · Cited by 2,266
The delayed rise of present-day mammals
Bininda-Emonds, O. R. P., Cardillo, M., Jones, K. E., MacPhee, R. D. E., Beck, R. M. D., Grenyer, R., Price, S. A., Vos, R. A., Gittleman, J. L., & Purvis, A. (2007) · Nature · doi:10.1038/nature05634 · Cited by 2,082
Lord, D., & Mannering, F. (2010) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2010.02.001 · Cited by 1,774
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2788613 · Cited by 1,685
Visualizing the effects of predictor variables in black box supervised learning models
Apley, D. W., & Zhu, J. (2020) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12377 · Cited by 1,535
Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study
Sallis, J. F., Cerin, E., Conway, T. L., Adams, M. A., Frank, L. D., Pratt, M., Salvo, D., Schipperijn, J., Smith, G., Cain, K. L., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01284-2 · Cited by 1,394
A mid-term analysis of progress toward international biodiversity targets
Tittensor, D. P., Walpole, M., Hill, S. L. L., Boyce, D. G., Britten, G. L., Burgess, N. D., Butchart, S. H. M., Leadley, P. W., Regan, E. C., Alkemade, R., et al. (2014) · Science · doi:10.1126/science.1257484 · Cited by 1,227
Hierarchical generalized additive models in ecology: an introduction with mgcv
Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019) · PeerJ · doi:10.7717/peerj.6876 · Cited by 1,225
Sauerbrei, W., Royston, P. J., & Binder, H. (2007) · Statistics in Medicine · doi:10.1002/sim.3148 · Cited by 1,169
Baayen, R. H., & Milin, P. (2010) · International journal of psychological research · doi:10.21500/20112084.807 · Cited by 1,151
Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial
Gilbert, P. B., Montefiori, D. C., McDermott, A. B., Fong, Y., Benkeser, D., Deng, W., Zhou, H., Houchens, C. R., Martins, K., Jayashankar, L., et al. (2022) · Science · doi:10.1126/science.abm3425 · Cited by 1,137
The WHO estimates of excess mortality associated with the COVID-19 pandemic
Msemburi, W., Karlinsky, A., Knutson, V., Aleshin-Guendel, S., Chatterji, S., & Wakefield, J. (2023) · Nature · doi:10.1038/s41586-022-05522-2 · Cited by 1,064
Parsimonious Mixed ModelsPreprint
Bates, D., Kliegl, R., Vasishth, S., & Baayen, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.04967 · Cited by 1,603
Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations
Fabricius, K. E., Langdon, C., Uthicke, S., Humphrey, C., Noonan, S., De’ath, G., Okazaki, R., Muehllehner, N., Glas, M. S., & Lough, J. M. (2011) · Nature Climate Change · doi:10.1038/nclimate1122 · Cited by 1,006
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook
Lee, D., Hosanagar, K., & Nair, H. S. (2018) · Management Science · doi:10.1287/mnsc.2017.2902 · Cited by 1,069
Solon-Biet, S. M., McMahon, A. C., Ballard, J. W. O., Ruohonen, K., Wu, L. E., Cogger, V. C., Warren, A., Huang, X., Pichaud, N., Melvin, R. G., et al. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2014.02.009 · Cited by 904
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Ziter, C. D., Pedersen, E. J., Kucharik, C. J., & Turner, M. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817561116 · Cited by 891
A Population-Based Study of Genes Previously Implicated in Breast Cancer
Hu, C., Hart, S. N., Gnanaolivu, R. D., Huang, H., Lee, K. Y., Na, J., Gao, C., Lilyquist, J., Yadav, S., Boddicker, N. J., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2005936 · Cited by 894
Global buffering of temperatures under forest canopies
De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F., Scheffers, B. R., Hylander, K., Luoto, M., Vellend, M., Verheyen, K., & Lenoir, J. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0842-1 · Cited by 787
Forest microclimate dynamics drive plant responses to warming
Zellweger, F., Frenne, P. D., Lenoir, J., Vangansbeke, P., Verheyen, K., Bernhardt‐Römermann, M., Baeten, L., Hédl, R., Berki, I., Brunet, J., et al. (2020) · Science · doi:10.1126/science.aba6880 · Cited by 865
Global prevalence of cerebral palsy: A systematic analysis
McIntyre, S. J., Goldsmith, S. C., Webb, A., Ehlinger, V., Hollung, S. J., McConnell, K., Arnaud, C., Smithers‐Sheedy, H., Oskoui, M., Khandaker, G., et al. (2022) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.15346 · Cited by 837
CellRank for directed single-cell fate mapping
Lange, M., Bergen, V., Klein, M., Setty, M. N., Reuter, B., Bakhti, M., Lickert, H., Ansari, M., Gote-Schniering, J., Schiller, H. B., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01346-6 · Cited by 783
Greater vulnerability to warming of marine versus terrestrial ectotherms
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L., & Sunday, J. M. (2019) · Nature · doi:10.1038/s41586-019-1132-4 · Cited by 780
Widespread deoxygenation of temperate lakes
Jane, S. F., Hansen, G. J. A., Kraemer, B. M., Leavitt, P. R., Mincer, J. L., North, R. L., Pilla, R. M., Stetler, J. T., Williamson, C. E., Woolway, R. I., et al. (2021) · Nature · doi:10.1038/s41586-021-03550-y · Cited by 716
What do we gain from simplicity versus complexity in species distribution models?
Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., McMahon, S. M., Normand, S., Thuiller, W., Wüest, R. O., Zimmermann, N. E., & Elith, J. (2014) · Ecography · doi:10.1111/ecog.00845 · Cited by 706
Large mammal population declines in Africa’s protected areas
Craigie, I. D., Baillie, J. E. M., Balmford, A., Carbone, C., Collen, B., Green, R. E., & Hutton, J. M. (2010) · Biological Conservation · doi:10.1016/j.biocon.2010.06.007 · Cited by 685
Coastal phytoplankton blooms expand and intensify in the 21st century
Dai, Y., Yang, S., Zhao, D., Hu, C., Xu, W., Anderson, D. M., Li, Y., Song, X., Boyce, D. G., Gibson, L., et al. (2023) · Nature · doi:10.1038/s41586-023-05760-y · Cited by 702
Baayen, R. H., Milin, P., Đurđević, D. F., Hendrix, P., & Marelli, M. (2011) · Psychological Review · doi:10.1037/a0023851 · Cited by 595
The relation between valence and arousal in subjective experience.
Kuppens, P., Tuerlinckx, F., Russell, J. A., & Barrett, L. F. (2013) · Psychological Bulletin · doi:10.1037/a0030811 · Cited by 583
The role of urban trees in reducing land surface temperatures in European cities
Schwaab, J., Meier, R., Mussetti, G., Seneviratne, S. I., Bürgi, C., & Davin, É. L. (2021) · Nature Communications · doi:10.1038/s41467-021-26768-w · Cited by 629
Modelling Palaeoecological Time Series Using Generalised Additive Models
Simpson, G. L. (2018) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2018.00149 · Cited by 567
Accurate intelligible models with pairwise interactions
Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564
Increasing frequency and intensity of the most extreme wildfires on Earth
Cunningham, C. X., Williamson, G. J., & Bowman, D. M. J. S. (2024) · Nature Ecology & Evolution · doi:10.1038/s41559-024-02452-2 · Cited by 663
Intelligible models for classification and regression
Lou, Y., Caruana, R., & Gehrke, J. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339556 · Cited by 547
Extreme High Temperatures and Hospital Admissions for Respiratory and Cardiovascular Diseases
Lin, S., Luo, M., Walker, R. J., Liu, X., Hwang, S., & Chinery, R. (2009) · Epidemiology · doi:10.1097/ede.0b013e3181ad5522 · Cited by 498
Gianola, D., & van Kaam, J. B. C. H. M. (2008) · Genetics · doi:10.1534/genetics.107.084285 · Cited by 457
Wong, C.-M., Vichit-Vadakan, N., Kan, H., & Qian, Z. (2008) · Environmental Health Perspectives · doi:10.1289/ehp.11257 · Cited by 476
Lake heatwaves under climate change
Woolway, R. I., Jennings, E., Shatwell, T., Gołub, M., Pierson, D. C., & MABERLY, S. C. (2021) · Nature · doi:10.1038/s41586-020-03119-1 · Cited by 503
Cross validation for model selection: A review with examples from ecology
Yates, L. A., Aandahl, Z., Richards, S. A., & Brook, B. W. (2022) · Ecological Monographs · doi:10.1002/ecm.1557 · Cited by 565
Monitoring Change in Vertebrate Abundance: the Living Planet Index
COLLEN, B., LOH, J., WHITMEE, S., McRAE, L., AMIN, R., & BAILLIE, J. E. M. (2009) · Conservation Biology · doi:10.1111/j.1523-1739.2008.01117.x · Cited by 441
Phenological shifts in lake stratification under climate change
Woolway, R. I., Sharma, S., Weyhenmeyer, G. A., Debolskiy, A. V., Gołub, M., Mercado‐Bettín, D., Perroud, M., Stepanenko, V., Tan, Z., Grant, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22657-4 · Cited by 454
Multiple movement modes by large herbivores at multiple spatiotemporal scales
Fryxell, J. M., Hazell, M. E., Börger, L., Dalziel, B. D., Haydon, D. T., Morales, J. M., McIntosh, T., & Rosatte, R. C. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801737105 · Cited by 428
Emotion and language: Valence and arousal affect word recognition.
Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014) · Journal of Experimental Psychology General · doi:10.1037/a0035669 · Cited by 471
Land-use intensity alters networks between biodiversity, ecosystem functions, and services
Felipe‐Lucia, M. R., Soliveres, S., Penone, C., Fischer, M., Ammer, C., Boch, S., Boeddinghaus, R. S., Bonkowski, M., Buscot, F., Fiore‐Donno, A. M., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016210117 · Cited by 453
The extent and drivers of gender imbalance in neuroscience reference lists
Dworkin, J. D., Linn, K. A., Teich, E. G., Zurn, P., Shinohara, R. T., & Bassett, D. S. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0658-y · Cited by 543
PCB pollution continues to impact populations of orcas and other dolphins in European waters
Jepson, P. D., Deaville, R., Barber, J. L., et al. (2016) · Scientific Reports · doi:10.1038/srep18573 · Cited by 420
Predictability of biomass burning in response to climate changes
Daniau, A. ‐., Bartlein, P. J., Harrison, S. P., Prentice, I. C., Brewer, S., Friedlingstein, P., Harrison‐Prentice, T. I., Inoue, J., Izumi, K., Marlon, J. R., et al. (2012) · Global Biogeochemical Cycles · doi:10.1029/2011gb004249 · Cited by 417
Inference in Multiscale Geographically Weighted Regression
Yu, H., Fotheringham, A. S., Li, Z., et al. (2019) · Geographical Analysis · doi:10.1111/gean.12189 · Cited by 414
Data-intensive science applied to broad-scale citizen science
Hochachka, W. M., Fink, D., Hutchinson, R., Sheldon, D. R., Wong, W., & Kelling, S. (2011) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2011.11.006 · Cited by 425
Handbook of Quantile Regression
(2017) · doi:10.1201/9781315120256 · Cited by 392
The Myth of Cognitive Decline: Non‐Linear Dynamics of Lifelong Learning
Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., & Baayen, R. H. (2014) · Topics in Cognitive Science · doi:10.1111/tops.12078 · Cited by 409
Climate change fingerprints in recent European plant phenology
Menzel, A., Yuan, Y., Matiu, M., Sparks, T. H., Scheifinger, H., Gehrig, R., & Estrella, N. (2020) · Global Change Biology · doi:10.1111/gcb.15000 · Cited by 415
Serial exploitation of global sea cucumber fisheries
Anderson, S. C., Flemming, J. M., Watson, R. A., & Lotze, H. K. (2010) · Fish and Fisheries · doi:10.1111/j.1467-2979.2010.00397.x · Cited by 400
What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
Lundberg, I., Johnson, R., & Stewart, B. (2021) · American Sociological Review · doi:10.1177/00031224211004187 · Cited by 422
An R package for an integrated evaluation of statistical approaches to cancer incidence projection
Knoll, M., Furkel, J., Debus, J., Abdollahi, A., Karch, A., & Stock, C. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01133-5 · Cited by 381
Montaner, J. S. G., Lima, V. D., Harrigan, P. R., Lourenço, L., Yip, B., Nosyk, B., Wood, E., Kerr, T. H., Shannon, K., Moore, D. M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087872 · Cited by 354
mgcv: Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
Wood, S. (2000) · doi:10.32614/cran.package.mgcv · Cited by 399
Self-citationDecoupling of SDGs followed by re-coupling as sustainable development progresses
Wu, X., Fu, B., Wang, S., Song, S., Li, Y., Xu, Z., Wei, Y., & Liu, J. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00868-x · Cited by 348
Collins, L., Bradstock, R. A., Clarke, H. G., Clarke, M. F., Nolan, R. H., & Penman, T. D. (2021) · Environmental Research Letters · doi:10.1088/1748-9326/abeb9e · Cited by 342
Video game play is positively correlated with well-being
Johannes, N., Vuorre, M., & Przybylski, A. K. (2021) · Royal Society Open Science · doi:10.1098/rsos.202049 · Cited by 330
Scheipl, F., Greven, S., & Küchenhoff, H. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.10.022 · Cited by 320
Duration of sleep contributes to next-day pain report in the general population
Edwards, R. R., Almeida, D. M., Klick, B., Haythornthwaite, J. A., & Smith, M. T. (2008) · Pain · doi:10.1016/j.pain.2008.01.025 · Cited by 321
Marques, T. A., Thomas, L., Ward, J., Dimarzio, N. A., & Tyack, P. L. (2009) · The Journal of the Acoustical Society of America · doi:10.1121/1.3089590 · Cited by 320
Morphological influences on the recognition of monosyllabic monomorphemic words
Baayen, R. H., Feldman, L. B., & Schreuder, R. (2006) · Journal of Memory and Language · doi:10.1016/j.jml.2006.03.008 · Cited by 302
Henderson, M. X., Cornblath, E. J., Darwich, A., Zhang, B., Brown, H., Gathagan, R. J., Sandler, R. M., Bassett, D. S., Trojanowski, J. Q., & Lee, V. M. Y. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0457-5 · Cited by 310
Global acceleration in rates of vegetation change over the past 18,000 years
Mottl, O., Flantua, S. G. A., Bhatta, K. P., Felde, V. A., Giesecke, T., Goring, S. J., Grimm, E. C., Haberle, S. G., Hooghiemstra, H., Ivory, S., et al. (2021) · Science · doi:10.1126/science.abg1685 · Cited by 319
Heterogeneous global crop yield response to biochar: a meta-regression analysis
Crane-Droesch, A., Abiven, S., Jeffery, S., & Torn, M. S. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/4/044049 · Cited by 297
Temperature-related biodiversity change across temperate marine and terrestrial systems
Antão, L. H., Bates, A. E., Blowes, S. A., Waldock, C. A., Supp, S. R., Magurran, A. E., Dornelas, M., & Schipper, A. M. (2020) · Nature Ecology & Evolution · doi:10.1038/s41559-020-1185-7 · Cited by 308
The contribution of insects to global forest deadwood decomposition
Seibold, S., Rammer, W., Hothorn, T., Seidl, R., Ulyshen, M. D., Lorz, J., Cadotte, M. W., Lindenmayer, D. B., Adhikari, Y. P., Aragón, R., et al. (2021) · Nature · doi:10.1038/s41586-021-03740-8 · Cited by 299
Post-fire vegetation and fuel development influences fire severity patterns in reburns
Coppoletta, M., Merriam, K. E., & Collins, B. M. (2016) · Ecological Applications · doi:10.1890/15-0225 · Cited by 295
Wei, Y., Wang, Y., Di, Q., Choirat, C., Wang, Y., Koutrakis, P., Zanobetti, A., Dominici, F., & Schwartz, J. D. (2019) · BMJ · doi:10.1136/bmj.l6258 · Cited by 288
Cross-boundary human impacts compromise the Serengeti-Mara ecosystem
Veldhuis, M. P., Ritchie, M. E., Ogutu, J. O., Morrison, T. A., Beale, C. M., Estes, A. B., Mwakilema, W., Ojwang, G. O., Parr, C. L., Probert, J., et al. (2019) · Science · doi:10.1126/science.aav0564 · Cited by 265
Bagchi, R., Swinfield, T., Gallery, R. E., Lewis, O. T., Gripenberg, S., Narayan, L., & Freckleton, R. P. (2010) · Ecology Letters · doi:10.1111/j.1461-0248.2010.01520.x · Cited by 266
Paulus, M. P., Squeglia, L. M., Bagot, K., Jacobus, J., Kuplicki, R., Breslin, F. J., Bodurka, J., Morris, A. S., Thompson, W. K., Bartsch, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.040 · Cited by 254
Cooper, T. F., De’ath, G., Fabricius, K., & Lough, J. (2007) · Global Change Biology · doi:10.1111/j.1365-2486.2007.01520.x · Cited by 268
Bégin, P., Callum, J., Jamula, E., Cook, R. J., Heddle, N. M., Tinmouth, A. T., Zeller, M. P., Beaudoin-Bussières, G., Amorim, L., Bazin, R., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01488-2 · Cited by 258
van Beest, F. M., Rivrud, I. M., Loe, L. E., Milner, J. M., & Mysterud, A. (2011) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2011.01829.x · Cited by 273
initiative, F. T. O. T. S., Sauerbrei, W., Perperoglou, A., Schmid, M. C., Abrahamowicz, M., Becher, H., Binder, H., Dunkler, D., Harrell, F. E., Royston, P. J., et al. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00074-3 · Cited by 270
Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe
Zellweger, F., Coomes, D. A., Lenoir, J., Depauw, L., Maes, S. L., Wulf, M., KIRBY, K. J., Brunet, J., Kopecký, M., Máliš, F., et al. (2019) · Global Ecology and Biogeography · doi:10.1111/geb.12991 · Cited by 254
A map of the topsoil organic carbon content of Europe generated by a generalized additive model
de Brogniez, D., Ballabio, C., Stevens, A., Jones, R., Montanarella, L., & van Wesemael, B. (2014) · European Journal of Soil Science · doi:10.1111/ejss.12193 · Cited by 247
Modeling avian full annual cycle distribution and population trends with citizen science data
Fink, D., Auer, T., Johnston, A., Ruiz‐Gutiérrez, V., Hochachka, W. M., & Kelling, S. (2019) · Ecological Applications · doi:10.1002/eap.2056 · Cited by 265
Butterfly abundance declines over 20 years of systematic monitoring in Ohio, USA
Wepprich, T., Adrion, J. R., Ries, L., Wiedmann, J. L., & Haddad, N. M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216270 · Cited by 251
Estimates of fishing gear loss rates at a global scale: A literature review and meta‐analysis
Richardson, K., Hardesty, B. D., & Wilcox, C. (2019) · Fish and Fisheries · doi:10.1111/faf.12407 · Cited by 265
Song, Y., Wang, X., Tan, Y., Wu, P., Sutrisna, M., Cheng, J. C. P., & Hampson, K. D. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6120397 · Cited by 255
Shahinfar, S., Meek, P., & Falzon, G. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101085 · Cited by 229
Analyzing the Time Course of Pupillometric Data
van Rij, J., Hendriks, P., van Rijn, H., Baayen, R. H., & Wood, S. N. (2019) · Trends in Hearing · doi:10.1177/2331216519832483 · Cited by 244
Self-citationCOMPONENTS OF UNCERTAINTY IN SPECIES DISTRIBUTION ANALYSIS: A CASE STUDY OF THE GREAT GREY SHRIKE
Dormann, C. F., Purschke, O., Márquez, J. R. G., Lautenbach, S., & Schröder, B. (2008) · Ecology · doi:10.1890/07-1772.1 · Cited by 220
Levy, D. T., Warner, K. E., Cummings, K. M., Hammond, D. G., Kuo, C. C.-L., Fong, G. T., Thrasher, J. F., Goniewicz, M. L., & Borland, R. (2018) · Tobacco Control · doi:10.1136/tobaccocontrol-2018-054446 · Cited by 220
Characterising and understanding emission sources using bivariate polar plots and k-means clustering
Carslaw, D. C., & Beevers, S. D. (2012) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.09.005 · Cited by 221
The cave of shadows: Addressing the human factor with generalized additive mixed models
Baayen, R. H., Vasishth, S., Kliegl, R. M., & Bates, D. M. (2017) · Journal of Memory and Language · doi:10.1016/j.jml.2016.11.006 · Cited by 238
Édeline, É., Carlson, S. M., Stige, L. C., Winfield, I. J., Fletcher, J. M., James, J. B., Haugen, T. O., Vøllestad, L. A., & Stenseth, N. C. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705908104 · Cited by 218
Baayen, R. H., Chuang, Y., Shafaei-Bajestan, E., & Blevins, J. P. (2019) · Complexity · doi:10.1155/2019/4895891 · Cited by 202
Metadata
- Authors
- Wood, S. N.
- Corresponding author
- Wood, Simon N.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2017-05-18
- 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
