Analysis of Data from Nonorthogonal Split

advertisement

Bayesian and Classical

Analysis of Multi-Stratum

Response Surface Designs

Steven Gilmour

Queen Mary, University of London

Peter Goos

Universiteit Antwerpen

Outline

Split-plot and other multi-stratum designs

“State-of-the-art” analysis of data

REML/generalized least squares

Problems

Estimation of variance components

Degrees of freedom

Three possible solutions

Fix value(s) of variance-component(s)

Use randomization-based estimation

Bayesian analysis

Multi-stratum designs

 Randomization of treatments to experimental units is restricted in such a way that particular sets of units must receive the same level of one or more treatment factors

Includes classical orthogonal split-plot, split-split-plot, crisscross, etc. designs (regular factorial treatment sets)

Also includes nonorthogonal designs with similar structures (irregular factorial or response surface treatment sets)

Are (nested) block designs with at least one main effect totally confounded with block effects

Often necessary when some factors are hard to change

Multi-stratum designs

I refer to the runs as units and the groups of units defined by the randomization restrictions as blocks , superblocks , …

Randomization is performed by randomly relabelling …, superblocks, blocks and units

Implies random effects for …, superblocks, blocks, units (error) in derived linear model

Fixed treatment effects can be modelled using usual polynomial response surface model

Freeze-dried coffee experiment

● Response: amount of retained volatile compounds in freeze-dried coffee

● Treatment factors:

Pressure in drying chamber (dial-controlled)

Heating temperature, Initial solids content, Slab thickness,

Freezing rate (all easy to change)

5 runs during each of 6 days

Randomization restricted so that all runs in a day have the same pressure

Freeze-dried coffee experiment

Block Press Temp Solids Thickn Rate Block Press Temp Solids Thickn Rate

1 1 0 0 0 1 4 1 0 0 -1 0

1 1 0 0 1 0 4 1 1 0 0 0

2

2

2

2

3

1

2

1

1

3

3

3

3

1

1

1

0

0

0

0

0

-1

-1

-1

-1

-1

-1

0

0

0

-1

1

1

-1

0

1

-1

-1

1

0

0

1

0

1

1

-1

-1

0

1

1

-1

-1

0

0

0

0

-1

1

-1

1

0

1

-1

1

-1

0

0

0

0

1

-1

-1

1

0

1

-1

-1

1

4

4

4

5

5

5

5

5

6

6

6

6

6

1

1

1

-1

-1

-1

-1

-1

0

0

0

0

0

0

0

0

0

1

1

-1

-1

1

0

1

-1

-1

0

-1

0

0

1

-1

1

-1

-1

0

1

1

-1

0

0

0

0

-1

1

1

-1

1

0

-1

1

-1

0

-1

1

-1

1

-1

-1

1

1

-1

0

0

0

Model and analysis

Model y f

   i

 y

X

  

Generalized least squares (GLS) estimation

' V

'

Variance-covariance matrix v r

ˆ

X

1

X

Model and analysis

Model y f

   i

 y

X

  

Generalized least squares (GLS) estimation

' V

'

?

'

ˆ

X

Variance-covariance matrix v r

ˆ

X

1 ?

v r

ˆ 

'

1

X

Variance component estimation

REML: REsidual Maximum Likelihood

Yields the same answers as ANOVA in orthogonal designs (e.g. standard split-plots)

Applicable when designs are not orthogonal (e.g. nonorthogonal split-plots)

State of the art in many disciplines

Available in many statistical software packages

Analysis

Different implementations:

Variance components allowed to be negative or not

Various methods for obtaining effective degrees of freedom

Estimates generally consistent with each other, given different implementations

Effective degrees of freedom can be inconsistent with each other

All methods can give surprising results

Freeze-dried coffee experiment

Estimates of whole-plot error variance

SAS: 0

GenStat: 0

R: 0.0051

Degrees of freedom for testing linear effect of pressure (full model)

SAS proc mixed with Kenward & Roger: 9 df

SAS proc mixed with containment method: 6 df

R lme default: 3 df

Freeze-dried coffee experiment

Simplified model:

Freeze-dried coffee experiment

Data are treated as if they come from a completely randomized experiment

OLS estimates are obtained

Degrees of freedom for testing linear effect of pressure are too optimistic

Upper bound for full second-order model: 3 df

Because of nonorthogonality: less than 3 df

SAS proc mixed with Kenward & Roger: 9 df

SAS proc mixed with containment method: 6 df

R lme default: 3 df

Artificial example

Block X1 X2 Y Block X1 X2 Y Block X1 X2 Y Block X1 X2 Y

1 -1 -1 11 2 -1 -1 10 3 1 -1 31 4 1 -1 40

1 -1 0 13 2 -1 0 20 3 1 0 38 4 1 0 40

1 -1 1 18 2 -1 1 23 3 1 1 33 4 1 1 41

Artificial example

Block X1 X2 Y Block X1 X2 Y Block X1 X2 Y Block X1 X2 Y

1 -1 -1 11 2 -1 -1 10 3 1 -1 31 4 1 -1 40

1 -1 0 13 2 -1 0 20 3 1 0 38 4 1 0 40

1 -1 1 18 2 -1 1 23 3 1 1 33 4 1 1 41

Solution II: Randomization-based analysis

 Even nonorthogonal multi-stratum designs have simple orthogonal block structures (if each block/superblock/... is the same size) [Nelder, 1965]

 Ignoring treatment structure, randomization-based analysis gives minimum variance unbiased estimators of variance components (pure error)

 Only assumption is that treatment and unit effects are additive

Randomization-based analysis

 Proposed analysis:

Use discrete treatments defined by combinations of factor levels (ignoring treatment model)

Anova gives correct estimates of variance components with correct degrees of freedom

Use these estimates to fit treatment model using

GLS

Base inferences on these estimates

“Extra sums of squares” represent lack of fit

 Not clear that GLS is best, but is same as with REML

Freeze-dried coffee experiment

3

3

2

3

3

3

1

2

1

1

2

2

2

WP Treat Press Temp Solids Thickn Rate WP Treat Press Temp Solids Thickn Rate

1

1

1

2

1

1

0

0

0

0

0

1

1

0

4

4

16

17

1

1

0

1

0

0

-1

0

0

0

3

4

5

6

7

8

9

1

1

1

0

0

0

0

-1

0

0

0

-1

1

1

0

0

1

0

1

1

-1

0

0

0

0

-1

1

-1

0

0

0

0

1

-1

-1

4

4

4

5

5

5

5

18

19

4

11

20

21

22

1

1

1

-1

-1

-1

-1

0

0

0

0

1

1

-1

0

-1

0

0

1

-1

1

0

0

0

0

-1

1

1

1

-1

-1

0

0

-1

0

10

11

12

13

14

15

0

-1

-1

-1

-1

-1

-1

0

1

-1

-1

1

-1

0

1

1

-1

-1

1

0

1

-1

1

-1

1

0

1

-1

-1

1

5

6

6

6

6

6

23

24

6

25

26

27

-1

0

0

0

0

0

-1

1

0

1

-1

-1

-1

-1

0

1

1

-1

-1

1

0

-1

1

-1

0

-1

1

1

1

-1

Freeze-dried coffee experiment

There are 27 treatments, 3 replicated twice

0 residual degrees of freedom for blocks

3 residual degrees of freedom for runs

Blocks variance component cannot be estimated, unit variance badly estimated

Full polynomial model can be fitted, but no global inference is possible

Weak inference is possible for all individual parameters except main effects of pressure

 This design is too small for a frequentist analysis

Solution III: Bayesian approach

 Advantages:

Takes into account uncertainty in prior beliefs

Prior beliefs can be contradicted by the data

No problems determining the appropriate degrees of freedom for hypothesis tests

WinBUGS software is free

The Bayesian approach

Requires a user-specified (joint) distribution for all model parameters (

,

2 ,

2 )

Posterior marginal distributions can be used for inference about parameters

 Results:

Similar to REML/GLS if data contain enough information

Similar to prior distribution if data don’t contain enough information

The Bayesian approach

Noninformative priors for

 r

: N(0,

)

Weakly informative priors for

Linear and interaction effects: N(0,25)

Quadratic effects: N(0,100) r

:

The Bayesian approach

 Variance components

The Bayesian approach

 Variance components weakly informative highly informative not informative

Results: linear effect of pressure

Linear effect of temperature

Interaction of slab thickness and freezing rate

Summary of results

Prior information on

 has little impact

Prior information on

2 not important at all

Some results strongly depend on prior information about

2

Hard-to-change factor coefficients

Sub-plot factor interaction coefficients that are not nearly orthogonal to whole plots

 Results for other coefficients insensitive to the choice of the prior for

2

Discussion

REML/GLS analysis can be misleading as it often leads to an analysis that ignores the multi-stratum nature of the design

Likelihood methods have good asymptotic properties, i.e. large numbers of units in each stratum, so should not be expected to work in small experiments

Problem is due to a lack of information in the blocks stratum

We should honestly admit that there is no information and/or provide prior information

Discussion

Randomization based analysis should always be done (in every experiment!) as a first step

Makes very few assumptions, so is much more robust than any other analysis

Provides a “reality check”

Might make extra assumptions unnecessary

Bayesian analysis can help

Prior information is taken into account

Prior information can be overruled

Depends heavily on prior assumptions, but these are clearly and honestly expressed

References

Multi-stratum response surface designs

Luzia A. Trinca and Steven G. Gilmour

Technometrics, 2001

A split-unit response surface design for improving aroma retention in freeze dried coffee

Steven G. Gilmour, J. Mauricio Pardo, Luzia A. Trinca, K.

Niranjan and Don Mottram

Proceedings of the 6th European Conference on Food-

Industry and Statistics, 2000

Analysis of data from unbalanced multi-stratum designs

Steven G. Gilmour and Peter Goos

Submitted

Download