Boosted Beta Regression

PLoS ONE

Volumn 8, Issue 4, 2013, Pages

  Schmid, Matthias ^a   Wickler, Florian ^b   Maloney, Kelly O ^c   Mitchell, Richard ^d   Fenske, Nora ^b   Mayr, Andreas ^a  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

^c U S GEOLOGICAL SURVEY   (United States)

^d U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; BENTHOS; BETA REGRESSION MODEL; CALCULATION; CONTROLLED STUDY; EPHEMEROPTERA; INTERMETHOD COMPARISON; LAKE; NONHUMAN; PREDICTION; SPECIES HABITAT; STATISTICAL ANALYSIS; STATISTICAL MODEL; HUMAN; METHODOLOGY; SPATIOTEMPORAL ANALYSIS; STATISTICAL DISTRIBUTION; STATISTICS AND NUMERICAL DATA;

HUMANS; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; RESEARCH DESIGN; SPATIO-TEMPORAL ANALYSIS; STATISTICAL DISTRIBUTIONS;

EID: 84876556361     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0061623     Document Type: Article

References (56)

1. Girao LC, Lopes AV, Tabarelli M, Bruna EM, (2007) Changes in tree reproductive traits reduce functional diversity in a fragmented atlantic forest landscape. PLOS ONE 2: e908.
2. Warton DI, Hui FKC, (2011) The arcsine is asinine: The analysis of proportions in ecology. Ecology 92: 3-10.
3. Laliberte E, Adair EC, Hobbie SE, (2012) Estimating litter decomposition rate in single-pool models using nonlinear beta regression. PLOS ONE 7: e45140.
4. Schlegel P, Havenhand JN, Gillings MR, Williamson JE, (2012) Individual variability in reproductive success determines winners and losers under ocean acidification: A case study with sea urchins. PLOS ONE 7: e53118.
5. Papke LE, Wooldridge JM, (1996) Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics 11: 619-632.
6. Ferrari SLP, Cribari-Neto F, (2004) Beta regression for modelling rates and proportions. Journal of Applied Statistics 31: 799-815.
7. Hunger M, Baumert J, Holle R, (2011) Analysis of SF-6D index data: Is beta regression appropriate? Value in Health 14: 759-767.
8. Seow WJ, Pesatori AC, Dimont E, Farmer PB, Albetti B, et al. (2012) Urinary benzene biomarkers and DNA methylation in Bulgarian petrochemical workers: Study findings and comparison of linear and beta regression models. PLOS ONE 7: e50471.
9. Kieschnick R, McCullough BD, (2003) Regression analysis of variates observed on (0, 1): Percentages, proportions and fractions. Statistical Modelling 3: 193-213.
10. Cribari-Neto F, Zeileis A (2010) Beta regression in R. Journal of Statistical Software 34: Issue 2.
11. Richards SA, (2008) Dealing with overdispersed count data in applied ecology. Journal of Applied Ecology 45: 218-227.
12. Jonsson MT, Thor G, (2012) Estimating coextinction risks from epidemic tree death: Affiliate lichen communities among diseased host tree populations of Fraxinus excelsior. PLOS ONE 7: e45701.
13. Peterson EE, Urquhart NS, (2006) Predicting water quality impaired stream segments using landscape-scale data and a regional geostatistical model: A case study in Maryland. Environmental Monitoring and Assessment 121: 615-638.
14. Gelfand AE, (2007) Guest editorial: Spatial and spatio-temporal modeling in environmental and ecological statistics. Environmental and Ecological Statistics 14: 191-192.
15. Schmid M, Hothorn T, Maloney KO, Weller DE, Potapov S, (2011) Geoadditive regression modelling of stream biological condition. Environmental and Ecological Statistics 18: 709-733.
16. Maloney KO, Schmid M, Weller DE, (2012) Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods in Ecology and Evolution 3: 116-128.
17. Simas AB, Barreto-Souza W, Rocha AV, (2010) Improved estimators for a general class of beta regression models. Computational Statistics & Data Analysis 54: 348-366.
18. Mayr A, Fenske N, Hofner B, Kneib T, Schmid M, (2012) Generalized additive models for location, scale and shape for high dimensional data- a exible approach based on boosting. Journal of the Royal Statistical Society, Series C 61: 403-427.
19. Rigby RA, Stasinopoulos DM, (2005) Generalized additive models for location, scale and shape (with discussion). Applied Statistics 54: 507-554.
20. Ripley BD (2004) Selecting amongst large classes of models. In: Adams N, Crowder M, Hand DJ, Stephens D, editors, Methods and Models in Statistics, London: Imperial College Press. 155-170.
21. Whittingham MJ, Stephens PA, Bradbury RB, Freckleton RP, (2006) Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology 75: 1182-1189.
22. Friedman JH, (2001) Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29: 1189-1232.
23. Bühlmann P, Hothorn T, (2007) Boosting algorithms: Regularization, prediction and model fitting (with discussion). Statistical Science 22: 477-522.
24. Kneib T, Hothorn T, Tutz G, (2009) Variable selection and model choice in geoadditive regression models. Biometrics 65: 626-634.
25. US Environmental Protection Agency (USEPA) (2009) National Lakes Assessment: A Collaborative Survey of the Nation's Lakes. Washington, D.C.: U.S. Environmental Protection Agency; Office of Water and Office of Research and Development. EPA 841-R-09-001.
26. Karr JR, Chu EW (1999) Restoring Life in Running Waters: Better Biological Monitoring. Washington D.C.: Island Press.
27. Barbour MT, Gerritsen J, Snyder BD, Stribling JB (1999) Rapid Bioassessment Protocols for Use in Streams and Wadeable Rivers: Periphyton, Benthic Macroinvertebrates and Fish. Washington, D.C.: U.S. Environmental Protection Agency; Office of Water, 2 edition. EPA 841-B-99-002.
28. Maloney KO, Feminella JW, (2006) Evaluation of single- and multi-metric benthic macroinvertebrate indicators of catchment disturbance over time at the Fort Benning Military Installation, Georgia, USA. Ecological Indicators 6: 469-484.
29. Smithson M, Verkuilen J, (2006) A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychological Methods 11: 54-71.
30. Cox C, (1996) Nonlinear quasi-likelihood models: Applications to continuous proportions. Computational Statistics & Data Analysis 21: 449-461.
31. Stasinopoulos DM, Rigby RA (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software 23: Issue 7.
32. Schmid M, Potapov S, Pfahlberg A, Hothorn T, (2010) Estimation and regularization techniques for regression models with multidimensional prediction functions. Statistics and Computing 20: 139-150.
33. Hastie T, Tibshirani R (1990) Generalized Additive Models. London: Chapman & Hall.
34. Wood S (2006) Generalized Additive Models: An Introduction with R. Boca Raton: Chapman & Hall/CRC.
35. Eilers PHC, Marx BD, (1996) Flexible smoothing with B-splines and penalties. Statistical Science 11: 89-121.
36. Schmid M, Hothorn T, (2008) Boosting additive models using component-wise P-splines. Computational Statistics & Data Analysis 53: 298-311.
37. McKay L, Bondelid T, Rea A, Johnston C, Moore R, et al. (2012) NHDPlus Version 2: User Guide. Available: ftp://ftp.horizon-systems.com/NHDPlus/NHDPlusV21/Documentation.
38. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F, (2006) World map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15: 259-263.
39. Davison A, Hinkley D (1997) Bootstrap Methods and their Application. Cambridge: Cambridge University Press.
40. Hothorn T, Leisch F, Zeileis A, Hornik K, (2005) The design and analysis of benchmark experiments. Journal of Computational & Graphical Statistics 14: 675-699.
41. Hothorn T, Buhlmann P, Kneib T, Schmid M, Hofner B (2012) mboost: Model-Based Boosting. Available: http://CRAN.R-project.org/package=mboost. R package version 2.2-1.
42. Hofner B, Mayr A, Fenske N, Schmid M (2012) gamboostLSS: Boosting Methods for GAMLSS Models. Available: https://r-forge.r-project.org/projects/gamboostlss. R package version 1.1-0.
43. Maddala GS (1983) Limited-Dependent and Qualitative Variables in Econometrics. Cambridge: Cambridge University Press.
44. Walsh CJ, Roy AH, Feminella JW, Cottingham PD, Groffman PM, et al. (2005) The urban stream syndrome: Current knowledge and the search for a cure. Journal of the North American Benthological Society 24: 706-723.
45. Mendoza G, Catalan J, (2010) Lake macroinvertebrates and the altitudinal environmental gradient in the Pyrenees. Hydrobiologia 648: 51-72.
46. Rahbek C, (1997) The relationship among area, elevation, and regional species richness in neotropical birds. The American Naturalist 149: 875-902.
47. Nyman M, Korhola A, Brooks SJ, (2005) The distribution and diversity of Chironomidae (Insecta: Diptera) in western Finnish Lapland, with special emphasis on shallow lakes. Global Ecology and Biogeography 14: 137-153.
48. Brodersen KR, Dall PC, Lindegaard C, (1998) The fauna in the upper stony littoral of Danish lakes: Macroinvertebrates as trophic indicators. Freshwater Biology 39: 577-592.
49. Welch C, Vodopich D, (1989) Production by Hexagenia limbata in a warm-water reservoir and its association with chlorophyll content of the water column. Hydrobiologia 185: 183-193.
50. Heino J, (2000) Lentic macroinvertebrate assemblage structure along gradients in spatial heterogeneity, habitat size and water chemistry. Hydrobiologia 418: 229-242.
51. Kilgour B, Clarkin C, Morton W, Baldwin R, (2008) Inuence of nutrients in water and sediments on the spatial distributions of benthos in Lake Simcoe. Journal of Great Lakes Research 34: 365-376.
52. Harper PP, Cloutier L, (1986) Spatial structure of the insect community of a small dimictic lake in the Laurentians (Quebec). Internationale Revue der gesamten Hydrobiologie und Hydrographie 71: 655-685.
53. Rossaro B, Marziali L, Cardoso AC, Solimini A, Free G, et al. (2007) A biotic index using benthic macroinvertebrates for Italian lakes. Ecological Indicators 7: 412-429.
54. Feminella JW, (2000) Correspondence between stream macroinvertebrate assemblages and 4 ecoregions of the southeastern USA. Journal of the North American Benthological Society 19: 442-461.
55. Hawkins C, Norris R, Gerritsen J, Hughes R, Jackson S, et al. (2000) Evaluation of the use of landscape classifications for the prediction of freshwater biota: synthesis and recommendations. Journal of the North American Benthological Society 19: 541-556.
56. Stasinopoulos M, Rigby B (2012) gamlss.dist: Distributions to be Used for GAMLSS Modelling. Available: http://CRAN.R-project.org/package=gamlss.dist. R package version 4.2-0.