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Bootstrap Confidence Intervals for Three-way Component Methods Henk A.L. Kiers University of Groningen The Netherlands

Bootstrap Confidence Intervals for Three-way Component Methods Henk A.L. Kiers University of Groningen The Netherlands. three- way data X. i = 1 . . . . . . I. SUBJECTs. K. OCCASIONS. k=1 . j=1 . . . . . . . J VARIABLES. three- way data X. i = 1 . . . . . . I.

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Bootstrap Confidence Intervals for Three-way Component Methods Henk A.L. Kiers University of Groningen The Netherlands

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  1. Bootstrap Confidence Intervals for Three-way Component Methods Henk A.L. Kiers University of Groningen The Netherlands

  2. three-way data X i = 1 . . . . . . I SUBJECTs K OCCASIONS k=1 j=1 . . . . . . . J VARIABLES

  3. three-way data X i = 1 . . . . . . I SUBJECTs K OCCASIONS k=1 j=1 . . . . . . . J VARIABLES • Three-way Methods:Tucker3Xa = AGa(CB) + EaA (IP), B(JQ), C (KR) component matricesGamatricized version of G (PQR)core arrayCP = Candecomp/ParafacXa = AGa(CB) + EaG (RRR) superdiagonal • Practice: • three-way methods applied to sample from population • goal: results should pertain to population

  4. Example (Kiers & Van Mechelen 2001): • scores of 140 individuals, on 14 anxiousness response scales in 11 different situations • Tucker3 with P=6, Q=4, R=3 (41.1%) Rotation: B, C, and Core to simple structure

  5. Results for example data Kiers & Van Mechelen 2001: B 

  6. C 

  7. Core 

  8. Is solutions stable? Is solution ‘reliable’? Would it also hold for population? Kiers & Van Mechelen report split-half stability results: Split-half results: rather global stability measures

  9. How can we assess degree of stability/reliability of individual results? • confidence intervals (CI) for all parameters • not readily available • derivable under rather strong assumptions (e.g., normal distributions, full identification) • alternative: • BOOTSTRAP

  10. BOOTSTRAP • distribution free • very flexible (CI’s for ‘everything’) • can deal with nonunique solutions • computationally intensive

  11. Bootstrap procedure: • Analyze sample data X (IJK) by desired method  sample outcomes  (e.g., A, B, C and G) • Repeat for b=1:500 • draw sample with replacement from I slabs of X Xb (IJK) • analyze bootstrap sample Xb in same way as sample outcomes b (e.g., Ab, Bb, Cb and Gb) • For each individual parameter : • 500 values available • range of 95% middle values  “95% percentile interval” ( Confidence Interval)

  12. Basic idea of bootstrap: • distribution in sample = nonparametric maximum likelihood estimate of population distribution • draw samples from estimated population distribution,just as actual sample drawn from population • From which mode do we resample? • Answer: mimic how we sampled from population • sample subjects from population  resample A-mode

  13. Three questions: • How deal with transformational nonuniqueness? • Are bootstrap intervals good approximations of confidence intervals? • How deal with computational problems (if any)? Lots of possibilities, depends on interpretation Not too bad Simple effective procedure

  14. 1. How to deal with transformational nonuniqueness? • identify solution completely • identify solution up to permutation/reflection •  for CP and Tucker3 • identify solution up to orthogonal transformations • identify solution up to arbitrary nonsingular transformations •  only for Tucker3

  15. Identify solution completely:  uniquely defined outcome parameters   bootstrap straightforward (CI’s directly available) CP and Tucker3 (principal axes or simple structure) - solution identified up to scaling/permutation Both cases: - further identification needed

  16. does not affect fit Identify solution up to permutation/reflection  outcome parameters b may differ much, but maybe only due to ordering or sign  bootstrap CI’s unrealistically broad !  how to make b’s comparable? Solution:  reorder and reflect columns in (e.g.) Bb, Cbsuch that Bb, Cb optimally resemble B, C

  17. e.g., two equally strong components  unstable order pros cons Completely identified direct bootstrap CI’s takes orientation, order, (too?!) seriously Identified up to perm./refl. more realistic solution cannot fully mimic sample & analysis process

  18. *) thanks to program by Patrick Groenen (procedure by Meulman & Heiser, 1983) Intermezzo What can go wrong when you take orientation too seriously? Two-way Example Data: 100 x 8 Data set PCA: 2 Components Eigenvalues: 4.04, 3.96, 0.0002, (first two close to each other) PCA (unrotated) solutions for variables (a,b,c,d,e,f,g,h) bootstrap 95% confidence ellipses*

  19. What caused these enormous ellipses? Look at loadings for data and some bootstraps: … leading to standard errors: ...

  20. Conclusion: solutions very unstable, hence: loadings seem very uncertain However …. Configurations of subsamples very similar So: Weshould’ve considered the whole configuration !

  21. Identify solution up to orthogonal transformations • Tucker3 solution with A, B, C columnwise orthonormal: •  any rotation gives same fit (counterrotation of core) •  outcome parameters b may differ much, but maybe only due to coincidental ‘orientation’ •  bootstrap CI’s unrealistically broad • Make b’s comparable: •  rotate Bb, Cb,Gb such that they optimally resemble B, C, G • How? • minimize f1(T)=||BbT–B||2 and f2(U)=||CbU–C||2 • counterrotate core: Gb(UT) • minimize f3(S)=||SGb–G||2 • use Bb* = BbT , Cb* = CbU, Gb* = SGb to determine 95%CI’s comparable across bootstraps

  22. Notes: • first choose orientation of sample solution (e.g., principal axes or other) • order of rotations (first B and C, then G): somewhat arbitrary, but may have effect

  23. Identify solution up to nonsingular transformations ....analogously.....  transform Bb, Cb,Gb so as to optimally resemble B, C, G

  24. Expectation: • the more transformational freedom used in bootstraps •  the smaller the CI’s • Example: • anxiety data set (140 subjects, 14 scales, 11 situations) • apply 4 bootstrap procedures • compare bootstrap se’s of all outcomes

  25. BootstrapMethod mean se (B) mean se (C) mean se (G) Principal Axes .085 .101 3.84 Simple Structure .085 .093 2.77 Orthog Matching .059 .088 2.20 Oblique Matching .055 .076 2.17 Some summary results:

  26. Now what CI’s did we find for Anxiety data Plot of confidence ellipses for first two and last two B components

  27. Confidence intervals for Situation Loadings

  28. A bit small.... Confidence intervals for Higest Core Values

  29. 2. Are bootstrap intervals good approximations of Confidence Intervals? • 95%CI should contain popul.values in 95% of samples •  “coverage” should be 95% • Answered by SIMULATION STUDY • Set up: • construct population with Tucker3/CP structure + noise • apply Tucker3/CP to population  population parameters • draw samples from population • apply Tucker3/CP to sample and construct bootstrap CI’s • check coverage: how many CI’s contain popul. parameter

  30. Design of simulation study: • noise: low, medium, high • sample size (I): 20, 50, 100 • 6 size conditions: (J=4,8, K=6,20, core: 222, 333, 432) • Other Choices • number of bootstraps: always 500 • number of populations: 10 • number of samples 10 • Each cell: 1010500 = 50000 Tucker3 or CP analyses(full design: 336=54 conditions)

  31. Should be close to 95% Here are the results

  32. Some details: ranges of values per cell in design (and associated se’s) • Some cells really low coverage • Most problematic cases in conditions with small I (I=20)

  33. 3. How deal with computational problems (if any) Is there a problem? Computation times per 500 boostraps: (Note: largest data size: 100  8  20) CP: min 4 s, max 452 s Tucker3 (SimpStr): min 3 s, max 30 s Tucker3 (OrthogMatch): min 1 s, max 23 s Problem most severe with CP

  34. How deal with computational problems for CP? • Idea: Start bootstraps from sample solution • Problem: May detract from coverage • Tested by simulation: • CP with 5 different starts per bootstrap • vs • Fast bootstrap procedure

  35. Results: • Fast method about 6 times faster (as expected) • Coverage • Optimal method: B: 95.5% C: 95.1% • Fast method: B: 95.3% C: 94.7% • Time gain enormous • Coverage hardly different

  36. Conclusion& Discussion • Bootstrap CI’s seem reasonable • Matching makes intervals smaller • Computation times for Tucker3 acceptable, for CP can be decreased by starting each bootstrap in sample solution

  37. some first tests show that this works • Conclusion &Discussion • What do bootstrap CI’s mean in case of matching? • 95% confidence for each value ? - chance capitalization - ignores dependence of parameters (they vary together) • Show dependence by bootstrap movie...!?! • Develop joint intervals (hyperspheres)...? • Sampling from two modes (e.g., A and C) ? some first tests show that this does not work

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