0
Overview New methods War stories Conclusions References
Statistical machismo and common sense
Ben Bolker, McMaster Univers...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
Acknowledgements
People Steve Walker, Mollie Brooks, Mike McCoy
Su...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
Statistical machismo
blog post by Brian McGill on Dynamic Ecology
...
Overview New methods War stories Conclusions References
Statistical machismo
blog post by Brian McGill on Dynamic Ecology
...
Overview New methods War stories Conclusions References
Statistical machismo
blog post by Brian McGill on Dynamic Ecology
...
Overview New methods War stories Conclusions References
Statistical machismo (2)
Slippery slope: what is good enough? (GLM...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
Intellectual satisfaction
Novelty
Seek elegant/rigorous solutions
...
Overview New methods War stories Conclusions References
Solve scientic and societal problems
Knuth: The important thing, o...
Overview New methods War stories Conclusions References
Solve scientic and societal problems
Knuth: The important thing, o...
Overview New methods War stories Conclusions References
Career advancement
get a job/get tenure/get funded
satisfy colleag...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
Robustness
less reliance on assumptions
(Box all models are wrong ...
Overview New methods War stories Conclusions References
Speed and scalability
just get a bigger computer, or a cluster
qua...
Overview New methods War stories Conclusions References
Interpretability (Warton and Hui, 2010)
connection to mechanistic/...
Overview New methods War stories Conclusions References
Increased correctness (O'Hara and Kotze, 2010; Warton and Hui, 201...
Overview New methods War stories Conclusions References
Statistical power/eciency
squeezing more out of data is always goo...
Overview New methods War stories Conclusions References
Handle new kinds of data / solve new problems
Hard to argue with t...
Overview New methods War stories Conclusions References
Ease of use
general, exible frameworks (maybe)
(or domain-specic s...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
User friendliness/interfaces
how hard should you try to make your ...
Overview New methods War stories Conclusions References
Technology transfer and software engineering
Technology transfer m...
Overview New methods War stories Conclusions References
Technology transfer and software engineering
Technology transfer m...
Overview New methods War stories Conclusions References
Responsibilities
Do you have a responsibility to
Fix bugs?
Provide...
Overview New methods War stories Conclusions References
Outline
1 Overview
Statistical machismo
Why do ecological statisti...
Overview New methods War stories Conclusions References
Spatial moment equations
general theoretical framework for ecologi...
Overview New methods War stories Conclusions References
Deconvolution: example
habitat preference
distance
habitat
0
1
0 5...
Overview New methods War stories Conclusions References
Estimating growth autocorrelation in unmarked individuals
(Brooks ...
Overview New methods War stories Conclusions References
Growth autocorrelation (cont.)
power analysis:
require 500 individ...
Overview New methods War stories Conclusions References
Mixed stock estimation
(Bolker et al., 2003, 2007; Okuyama and Bol...
Overview New methods War stories Conclusions References
A cautionary example
Mismatch between simple
Gibbs sampler with
eq...
Overview New methods War stories Conclusions References
Mixed models
(generalized) linear mixed
models
very useful framewo...
Overview New methods War stories Conclusions References
Conclusions
Can't be too gloomy . . . trickle-down process does wo...
Overview New methods War stories Conclusions References
Overview New methods War stories Conclusions References
References
Angrist, J.D. and Pischke, J.S., 2009. Mostly
Harmless ...
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Montpellier

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Transcript of "Montpellier"

  1. 1. Overview New methods War stories Conclusions References Statistical machismo and common sense Ben Bolker, McMaster University Departments of Mathematics & Statistics and Biology ISEC July 2014
  2. 2. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  3. 3. Overview New methods War stories Conclusions References Acknowledgements People Steve Walker, Mollie Brooks, Mike McCoy Support NSERC Discovery grant
  4. 4. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  5. 5. Overview New methods War stories Conclusions References Statistical machismo blog post by Brian McGill on Dynamic Ecology ≈ method ageism (M. Brewer) criticizes unnecessarily fancy statistics, e.g. Bonferroni corrections phylogenetic corrections spatial regression estimation of detectability Bayesian methods Also cf. Murtaugh (2007; 2009; 2014)
  6. 6. Overview New methods War stories Conclusions References Statistical machismo blog post by Brian McGill on Dynamic Ecology ≈ method ageism (M. Brewer) criticizes unnecessarily fancy statistics, e.g. Bonferroni corrections phylogenetic corrections spatial regression estimation of detectability Bayesian methods Also cf. Murtaugh (2007; 2009; 2014)
  7. 7. Overview New methods War stories Conclusions References Statistical machismo blog post by Brian McGill on Dynamic Ecology ≈ method ageism (M. Brewer) criticizes unnecessarily fancy statistics, e.g. Bonferroni corrections phylogenetic corrections spatial regression estimation of detectability Bayesian methods Also cf. Murtaugh (2007; 2009; 2014)
  8. 8. Overview New methods War stories Conclusions References Statistical machismo (2) Slippery slope: what is good enough? (GLM vs ANOVA) McGill: And when the p-values are 0.0000001 and unlikely to change . . . Criticizing dogma, not methods per se Does this apply to statistical ecologists? Are we enabling bad practice? Caveat: researcher/teacher vs. researcher/consultant niche
  9. 9. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  10. 10. Overview New methods War stories Conclusions References Intellectual satisfaction Novelty Seek elegant/rigorous solutions Mathematical or computational challenges
  11. 11. Overview New methods War stories Conclusions References Solve scientic and societal problems Knuth: The important thing, once you have enough to eat and a nice house, is what you can do for others, what you can contribute to the enterprise as a whole Basic science Hypothesis testing (broad sense) Links to ecological theory Applied science Prediction Decision analysis Technology transfer (where possible)
  12. 12. Overview New methods War stories Conclusions References Solve scientic and societal problems Knuth: The important thing, once you have enough to eat and a nice house, is what you can do for others, what you can contribute to the enterprise as a whole Basic science Hypothesis testing (broad sense) Links to ecological theory Applied science Prediction Decision analysis Technology transfer (where possible)
  13. 13. Overview New methods War stories Conclusions References Career advancement get a job/get tenure/get funded satisfy colleagues be either useful or rigorous! avoid too much collaboration curse of novelty
  14. 14. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  15. 15. Overview New methods War stories Conclusions References Robustness less reliance on assumptions (Box all models are wrong . . . ) computational robustness handle crappy (small, noisy, missing . . . ) data robust to user error examples: sandwich estimators; M-estimators; design-based statistics; permutation tests tradeos: complexity, loss of interpretability, increased bias, decreased eciency
  16. 16. Overview New methods War stories Conclusions References Speed and scalability just get a bigger computer, or a cluster quantitative becomes qualitative modern methods (ensembles, resampling, MCMC . . . ) increase need for speed (but usually trivially parallelizable) scalability (parallel implementations; computational complexity) examples: ensemble-based approaches; kernel-based methods; sparse matrix computation; map/reduce tradeos: complexity, lack of generality
  17. 17. Overview New methods War stories Conclusions References Interpretability (Warton and Hui, 2010) connection to mechanistic/theoretical models . . . or to statistical paradigms (e.g. linear models) maybe a hard sell examples: Bayesian statistics (P(H|D) vs. P(D|H)); GLMs counterargument: Breiman (2001)
  18. 18. Overview New methods War stories Conclusions References Increased correctness (O'Hara and Kotze, 2010; Warton and Hui, 2010) decrease bias/type I error; improve coverage how big is bias generally? how much does it matter? unpopular with ecologists! especially if it lowers power/makes eects disappear . . .
  19. 19. Overview New methods War stories Conclusions References Statistical power/eciency squeezing more out of data is always good . . . but how much? maybe irrelevant if there's enough data (econometrics, e.g. Angrist and Pischke (2009)) examples: GLMs vs. least-squares; random vs xed eects tradeos: complexity, model-dependence/loss of robustness
  20. 20. Overview New methods War stories Conclusions References Handle new kinds of data / solve new problems Hard to argue with this one! Most often, methods for combining dierent characteristics: spatial/temporal/phylog. correlation non-Normal data missing data regularization/smoothing/dimension limitation etc. ...
  21. 21. Overview New methods War stories Conclusions References Ease of use general, exible frameworks (maybe) (or domain-specic solutions) interfaces examples: MaxEnt; information-theoretic approaches counterexamples: BUGS/JAGS, AD Model Builder?
  22. 22. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  23. 23. Overview New methods War stories Conclusions References User friendliness/interfaces how hard should you try to make your methods usable? (Dynamic Ecology blog post) what kind of interface? equations pseudocode code blobs package GUI
  24. 24. Overview New methods War stories Conclusions References Technology transfer and software engineering Technology transfer might be the most useful we can be Boring? Unrewarding? New tech has to come from somewhere statistical software now commoditized and often free good statistical software takes a lot of time and eort (and few users want to pay for it) eating dog food/developer blindness building software exposes new issues: Knuth: Science is what we understand well enough to explain to a computer. Art is everything else we do
  25. 25. Overview New methods War stories Conclusions References Technology transfer and software engineering Technology transfer might be the most useful we can be Boring? Unrewarding? New tech has to come from somewhere statistical software now commoditized and often free good statistical software takes a lot of time and eort (and few users want to pay for it) eating dog food/developer blindness building software exposes new issues: Knuth: Science is what we understand well enough to explain to a computer. Art is everything else we do
  26. 26. Overview New methods War stories Conclusions References Responsibilities Do you have a responsibility to Fix bugs? Provide features? Advise users? Does the no liability clause of free software licenses (e.g. GPL §16) absolve us of moral responsibility?
  27. 27. Overview New methods War stories Conclusions References Outline 1 Overview Statistical machismo Why do ecological statistics? 2 New methods Desiderata Statisticians and software 3 War stories 4 Conclusions
  28. 28. Overview New methods War stories Conclusions References Spatial moment equations general theoretical framework for ecological dynamics of spatial point processes (Ovaskainen et al., 2014) used mostly to understand qualitative ecological dynamics framework for connecting spatial theory and data? in particular, should be able to deconvolve eects of (e.g.) habitat and habitat preference N(x) = E(y − x)H(y) dy = (E ∗ H)(x) then ˜H(ω) = CEN CEE
  29. 29. Overview New methods War stories Conclusions References Deconvolution: example habitat preference distance habitat 0 1 0 50 correlations preference distance population so far just a gleam in my eye many details (non-Normality, nonlinear response) . . .
  30. 30. Overview New methods War stories Conclusions References Estimating growth autocorrelation in unmarked individuals (Brooks et al., 2013) uncorrelated growth: σ2(t) linear correlated growth: σ2(t) quadratic straightforward estimation
  31. 31. Overview New methods War stories Conclusions References Growth autocorrelation (cont.) power analysis: require 500 individuals @ 25% autocorrelation, 100 individuals @ 50% autocorrelation available data: 550 individuals so far unused (cf. Lavine et al. (2002))
  32. 32. Overview New methods War stories Conclusions References Mixed stock estimation (Bolker et al., 2003, 2007; Okuyama and Bolker, 2005) Bayesian mixed stock analysis, following Pella and Masuda (2001) unconditional likelihood: account for sampling error in sources better condence intervals many-to-many methods (Chen et al., 2010) widely used . . . q q q q q q q q q q q q q q q q q AS AV CR FL GB GG MX BR SU TR BAH BAR FLF CBG MOC NIC BRF
  33. 33. Overview New methods War stories Conclusions References A cautionary example Mismatch between simple Gibbs sampler with equivalent WinBUGS implementation Bug (??) in relatively widely used software; haven't found time to diagnose/x it! Density 051015 0.0 0.2 0.4 0.6 0.8 1.0 NWFL NWFL 0.00.51.01.5 SOFL NWFL 0246810 NWFL SOFL 0.0 0.2 0.4 0.6 0.8 1.0 0.00.51.01.5 SOFL SOFL tmcmc wbugs wbugsL wbugsLL
  34. 34. Overview New methods War stories Conclusions References Mixed models (generalized) linear mixed models very useful framework: GLMs (exponential family) + random eects linear algebra magic by D. Bates, M. Maechler technology transfer amazingly popular! q q q q q q q q q q q q q q q q q q q q q q q q q q 1995 2000 2005 2010 2015 year citations w q q GLMM paper all others
  35. 35. Overview New methods War stories Conclusions References Conclusions Can't be too gloomy . . . trickle-down process does work But we should at least recognize when we are amusing ourselves, and when we are doing science Can we formalize these ideas? Is it worth it?
  36. 36. Overview New methods War stories Conclusions References
  37. 37. Overview New methods War stories Conclusions References References Angrist, J.D. and Pischke, J.S., 2009. Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press, Princeton, 1 edition edition. ISBN 9780691120355. Bolker, B., Okuyama, T., et al., 2003. Ecological Applications, 13(3):763775. Bolker, B.M., Okuyama, T., et al., 2007. Molecular Ecology, 16:685695. doi:10.1111/j.1365-294X.2006.03161.x. Breiman, L., 2001. Statistical Science, 16(3):199215. ISSN 08834237. Brooks, M.E., McCoy, M.W., and Bolker, B.M., 2013. PLoS ONE, 8(10):e76389. doi:10.1371/journal.pone.0076389. Chen, Y., Smith, S.J., and Campana, S.E., 2010. Canadian Journal of Fisheries and Aquatic Sciences, 67(10):15331548. ISSN 0706-652X, 1205-7533. doi:10.1139/F10-078. Lavine, M., Beckage, B., and Clark, J.S., 2002. Journal of Agricultural, Biological, and Environmental Statistics, 7(1):2141. ISSN 1085-7117, 1537-2693. doi:10.1198/108571102317475044. Murtaugh, P.A., 2007. Ecology, 88(1):5662. , 2009. Ecology Letters, 12(10):10611068. ISSN 1461023X. doi:10.1111/j.1461-0248.2009.01361.x. , 2014. Ecology, 95(3):611617. ISSN 0012-9658. doi:10.1890/13-0590.1. O'Hara, R.B. and Kotze, D.J., 2010. Methods in Ecology and Evolution, 1(2):118122. ISSN 2041-210X. doi:10.1111/j.2041-210X.2010.00021.x. Okuyama, T. and Bolker, B.M., 2005. Ecological Applications, 15(1):315325. Ovaskainen, O., Finkelshtein, D., et al., 2014. Theoretical Ecology, 7(1):101113. ISSN 1874-1738, 1874-1746. doi:10.1007/s12080-013-0202-8. Pella, J. and Masuda, M., 2001. Fisheries Bulletin, 99:151167. Warton, D.I. and Hui, F.K.C., 2010. Ecology, 92(1):310. ISSN 0012-9658. doi:10.1890/10-0340.1.
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