mixed model analysis of highly correlated data: tales from the dark side of forestry
DESCRIPTION
Mixed Model Analysis of Highly Correlated Data: Tales from the Dark Side of Forestry. Christina Staudhammer, PhD candidate Valerie LeMay, PhD Thomas Maness, PhD Robert Kozak, PhD THE UNIVERSITY OF BRITISH COLUMBIA VANCOUVER, BRITISH COLUMBIA, CANADA. Introduction - 1. - PowerPoint PPT PresentationTRANSCRIPT
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Mixed Model Analysis of Highly Correlated Data:
Tales from the Dark Side of Forestry
Christina Staudhammer, PhD candidateChristina Staudhammer, PhD candidateValerie LeMay, PhDValerie LeMay, PhDThomas Maness, PhDThomas Maness, PhDRobert Kozak, PhDRobert Kozak, PhDTHE UNIVERSITY OF BRITISH COLUMBIA VANCOUVER, BRITISH COLUMBIA, CANADA
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Staudhammer, et al.Staudhammer, et al.
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Staudhammer, et al.Staudhammer, et al.
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Introduction - 1• Current Statistical Process
Control (SPC) in Sawmills – Data Collection:
• Periodically, a few boards are pulled from a machine
• Thickness measured in 6-10 places with digital calipers
– Data Analysis• Control Charts are constructed
to ensure that X, s2b, s2
w are within a target range, e.g.,
xsX 3
–SPC is slow and labour-intensive, but important and effective
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Introduction - 2• Recent advances in SPC
– Laser Range Sensors • Real-time measurements available
at up to 1000 meas./sec.• Each and every board (or cant) is
measured– Research describing Rigid Body
Motion• Removes effect of ‘bouncing
boards’ (or cants) • enables board profiles to be
analyzed, in addition to thickness– On-line machine diagnostics can be
monitored to trace quality problems to specific saws
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Interesting Issues• A great increase in the amount of information available
– the data from these devices is subject to noise• External, e.g., wane• Internal, e.g., measurement errors
– The data are closely spaced and highly autocorrelated• Boards are almost censused• Observations are easily predicted from their neighbors. • The process variance is underestimated, leading to too narrow
control limits for SPC and false signals of an out of control process.
• An adequate statistical model to describe the data has not yet been described in the literature.
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Objectives• Research Objective
– To establish a system for collecting and processing real-time quality control data for automated lumber manufacturing
• Presentation Objective– To present methods for estimation of the
components of variance so that control charts can be constructed
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Data Collection
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Profile Data
Profiles (y1 – y4) are computed using the laser readings and the known distance to the centre of the board.
l4
l2
Laser 3
l1
y3 y4
y1 y2
Laser 2
Laser 4
Laser 1
l3
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Sample Data - ProfileBoard 001 (side one)
(reduced data set: 50 observations/laser, side, board)
970
980
990
1000
1010
1020
1030
0 2 4 6 8Distance along Board (ft)
Prof
ile (0
.001
inch
)
laser 1 (bottom)
laser 3 (top)
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Simple Modelyijkm = + i + j + k + ijkm [1]
where:i = 1 to b boards;
j = 1 to s sides; k = 1 to r laser positions;
m = 1 to n measurements along the board;i = the ith board effect;j = the jth side effect;k = the kth laser position effect; andijkm = the error associated with the mth measurement.
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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
Model Details
• All effects are random, except sides• Observations on a single side of a board
are highly correlated, and thus the error covariance structure should be added to the model…
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Error Covariance StructuresIsotropic spatial covariance structurese.g., Exponential:
(Other models include Gaussian, spherical, linear)
Autoregressive covariance structures
e.g., ARMA(1,1):
Anisotropic spatial covariance structurese.g., Power:
)/exp(),cov( 2 ijji d
)(1)(1),cov( 12 jijijiji
),,(),,(2),cov( yjidy
xjidxji
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Model Fitting Methods• Models fit:
[1] Simple Model[2] Model [1] plus isotropic spatial error covariance
structure[3] Model [1] plus autoregressive error cov. structure[4] Model [1] plus anisotropic spatial error covariance
structure• Models were fit with SAS PROC MIXED • A reduced dataset was used with 50 meas. per
laser/side/board
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Model Evaluation• Tests for Maximum Likelihood Estimation (MLE)
– e.g., Likelihood Ratio Test, Wald Test, etc.– Are tests appropriate?
• Fit Statistics for MLE– Information Criteria, e.g., Akaike’s Information Criteria
(AIC) – Do not require setting arbitrary significance levels
model in the parameters ofnumber theis and ;likelihood log maximized empirical theis )|ˆ(
2)|ˆ(log2
Kx
KxAIC
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Results
Anisotropic PowerLaser Spatial
Model AIC Board Position Range x y
[1] 51,235 188.4 0.73 539.0
[2] 42,331 130.6 0.00 b 671.3 12.6c 1444.0 110.6
[3] 42,531 102.8 0.00 b 710.0 0.923 0.848c 1399.4 0.981 0.976
[4] 41,724 119.3 0.00 b 648.2 0.930 0.581c 1109.2 0.991 0.967
Covariance Parameter Parameters Parameters
Residual
ARMA
Simple Model
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Anisotropic PowerLaser Spatial
Model AIC Board Position Range x y
[1] 51,235 188.4 0.73 539.0
[2] 42,331 130.6 0.00 b 671.3 12.6c 1444.0 110.6
[3] 42,531 102.8 0.00 b 710.0 0.923 0.848c 1399.4 0.981 0.976
[4] 41,724 119.3 0.00 b 648.2 0.930 0.581c 1109.2 0.991 0.967
Covariance Parameter Parameters Parameters
Residual
ARMA
Results
Model [1] Plus Exponential Error Covariance Structure
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Anisotropic PowerLaser Spatial
Model AIC Board Position Range x y
[1] 51,235 188.4 0.73 539.0
[2] 42,331 130.6 0.00 b 671.3 12.6c 1444.0 110.6
[3] 42,531 102.8 0.00 b 710.0 0.923 0.848c 1399.4 0.981 0.976
[4] 41,724 119.3 0.00 b 648.2 0.930 0.581c 1109.2 0.991 0.967
Covariance Parameter Parameters Parameters
Residual
ARMA
Results
Model [1] plus ARMA(1,1) Error Cov. Structure
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Results
Anisotropic PowerLaser Spatial
Model AIC Board Position Range x y
[1] 51,235 188.4 0.73 539.0
[2] 42,331 130.6 0.00 b 671.3 12.6c 1444.0 110.6
[3] 42,531 102.8 0.00 b 710.0 0.923 0.848c 1399.4 0.981 0.976
[4] 41,724 119.3 0.00 b 648.2 0.930 0.581c 1109.2 0.991 0.967
Covariance Parameter Parameters Parameters
Residual
ARMA
Model [1] plus Anisotropic Power Error Cov. Structure
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• Model selection based on fit statistics– Lowest AIC indicates [4] with Anisotropic
Power Structure– What is indicated by directional variograms?
Discussion - 1
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Semivariograms vs. Model [4]
0
200
400
600
800
1000
1200
0 10 20 30 40 50 60 70
Distance (inches)
Sem
ivar
iogr
am
FittedVariogram011
015
029
038
065
081
102
107
109
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Semivariograms vs. Model [4]
0
200
400
600
800
1000
1200
1400
1600
1800
0 10 20 30 40 50 60
Distance (inches)
Sem
ivar
iogr
amFitted Variogram
010
011
014
015
020
022
029
030
034
036
038
043
049
051
053
055
059
065
067
072
075
081
101
102
103
107
109
110
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• Model selection based on knowledge of system– Appropriateness of isotropic spatial vs.
anisotropic spatial vs. autoregressive models of error covariance structure
– Should there be a decrease in between-board variance component?
• Will a saw travelling at varying speeds yield a consistent ‘saw signature’?
Discussion - 2
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Conclusions• Application of QC to automated processes
is an important step toward more efficient lumber processing
• Model selection should be based on knowledge of the system as well as fit statistics
• Further testing should be done on datasets from different days/saws to ensure widespread applicability
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Acknowledgements• National Science and Engineering
Research Council• British Columbia Science Council• Izaak Walton Killam Foundation• Canadian Forest Products• Weyerhauser Company• Forintek Canada