I am changing the title because this is really about the history of

anova,

and about strata in analysis of variance. As this kind of question

has been

arising very frequently, an extended comment may be in order.

The ideas, and the sums of squares breakdowns, go back to Fisher; see

in particular his "Design of Experiments", first published in 1935.

This

book is still a good read. I'm not sure that Fisher talked about

error strata,

but he certainly did emphasize the need to choose the error sum of

squares

that was appropriate to the inference that was of interest.

It is unfortunate that, in later developments, analysis of variance

was often

presented in a manner that forgot important parts of Fisher's

insights. One

of my complaints about the data mining community is that it seems

remarkably resistant to the notion that the distribution of the error

term in a

model can matter a lot.

Thus in an experiment that has blocks, plots within blocks and subplots

(I'll use as an example the kiwishade data set in the DAAG package),

treatments that are assigned at random to subplots are compared using

the

within plot residual (this ignores the possibility of a

plot:subplotTreatment

interaction, so that such designs must be used with care and/or

finesse),

treatments that are applied to whole plots are compared using the within

block residual, and so on.

In the kiwishade experiment, there were three blocks, four plots per

block

(one for each of 4 treatments) and four subplots (vines) per treatment.

See Maindonald and Braun (the book we've been calling DAAGUR),

pp.230-239, for a detailed exposition of the analysis of these data,

comparing the aov() output with the lme() output. [Details of this

and other

R-related books can be obtained from a CRAN site.]

For this experiment, the error structure is hierarchical, and it thus

makes sense

to equate strata with levels, in the sense in which this word is used

in the

documentation for lme(). The strata are: within plots [level 0 in lme

()], within

blocks between plots (level 1), and between blocks (level 2). To see

the

aov() output, type:

aov(yield ~ shade + Error(block/plot), data=kiwishade)

[Blocks might alternatively be regarded as a fixed effect.]

To understand the use of the between plot sum of squares, observe

that there

are 12 independent pieces of information at the plot level, i.e. 12

plots.

However, 2 (3-1) of these can be explained as due to differences

between blocks,

and 3 of them can be explained as due to differences between treatments.

Thus, we are left with 7 independent (in an algebraic sense) items of

information.

The corresponding error variance [the usual sum of squares about the

mean

divided by number of values less 1] has 6 degrees of freedom.

The within plot residual mean square has 36 degrees of freedom. This

experiment

did not have treatment that was applied at the subplot level, and

this is not the

correct mean squares for any treatment comparison. The analysis is

readily

simplified by calculating plot means, and basing all analyses on

those. In just the

same way:

1) treatments may be applied to whole apples, there may be multiple

measurements on an apple, and a reasonable way to do the analysis may be

to calculate means for each apple.

2) Commonly, in laboratory experiments, treatments are applied to whole

samples, and multiple measurements or assays are made on the one sample.

3) In a comparison of prescribing habits between registrars and

specialists,

there might be data from 50 registrars and 50 specialists in each

hospital,

but perhaps only 3 hospitals. So, do we have

(a) 3 pieces of information (3 hospitals with two items to compare

per hospital;

there'll be 2 degrees of freedom for the mean square), or

(b) closer to 300 [a split plot type comparison will give a mean

square with

(50+50-2)*3 degrees of freedom].

John Maindonald email:

[hidden email]
phone : +61 2 (6125)3473 fax : +61 2(6125)5549

Mathematical Sciences Institute, Room 1194,

John Dedman Mathematical Sciences Building (Building 27)

Australian National University, Canberra ACT 0200.

Genstat's achievement was to push to the limit, based on ideas of JA

Nelder

(a theory) and GN Wilkinson (an algorithm), ideas of balance that

made it

possible to retain the conceptual simplicity of analysis of variance

type strata.

Estimates of effects of interest may for crossed designs use an error

term that

combines variances across strata, so that "t"-statistics (with a

fudge used to get

degrees of freedom) may at best be reasonably approximated by a t-

distribution.

The methodology that is used in lme(), and other software that uses a

similar

approach, gives up the attempt to form an analysis of variance table.

The

model specifies the variance-covariance structure, and an important

part of the

analysis is the estimation of that variance-covariance structure.

Estimates of

effects that are of interest no longer have a t-distribution under

the standard

normality etc assumptions. Standard fudges (calculate a df for a t-

distribution

approximation) often work quite well. Doug Bates is right to keep

reminding

everyone that they are fudges.

The fudges are important for another more pervasive reason. It is

important

to know whether a variance is estimated with 2 or with ~300 degrees of

freedom. If there are only 2 pieces of information the normality

assumptions

matter a lot. Was one of those hospitals an outlier? Problems with

the fudge

used for calculate degrees of freedom for a t-approximation may be

the least

of the matters that should be a concern.

There's a nice account of Fisher's contribution to experimental design

(as well as a few complaints about some of Fisher's comments on

inference) in:

R. A. Fisher and Experimental Design: A Review

D. A. Preece

Biometrics, Vol. 46, No. 4 (Dec., 1990) , pp. 925-935

John Maindonald

On 15 Feb 2006, at 10:00 PM,

[hidden email] wrote:

> From: Peter Dalgaard <

[hidden email]>

> Date: 15 February 2006 3:26:27 AM

> To: WPhantom <

[hidden email]>

> Cc:

[hidden email]
> Subject: Re: [R] A concrete type I/III Sum of square problem

>

>

> WPhantom <

[hidden email]> writes:

>

>> Thanks Brian for the reference.

>> I just discover that it is available in our

>> library so I going to take it & read it soon.

>> Actually, I don't even know the difference

>> between a multistratum vs a single-stratum AOV. A

>> quick search on google returned me the R materials so that I imagine

>> that these concepts are quite specific to R.

>

> You have to be careful not to confuse Google's view of the world with

> Reality...

>

> The concept of error strata is much older than R, and existed for

> instance in Genstat, anno 1977 or so. However, Genstat seems to have

> left little impression on the Internet.

>

>> I will read the book first before asking for more informations.

>

> The executive summary is that the concept of error strata relies

> substantially on having a balanced design (at least for the random

> effects), so that the analysis can be decomposed into analyses of

> means, contrasts, and contrasts of means. For unbalanced designs, you

> usually get meaningless analyses.

>

>

>> Thanks

>>

>> Sylvain Clément

>>

>> At 12:38 14/02/2006, you wrote:

>>> More to the point, you are confusing

>>> multistratum AOV with single-stratuam AOV. For

>>> a good tutorial, see MASS4 (bibliographic

>>> information in the R FAQ). For unbalanced data

>>> we suggest you use lme() instead.

>>>

>>> --

>>> Brian D. Ripley,

[hidden email]
>>

>> ______________________________________________

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>>

https://stat.ethz.ch/mailman/listinfo/r-help>> PLEASE do read the posting guide!

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>> guide.html

>>

>

> --

> O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B

> c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K

> (*) \(*) -- University of Copenhagen Denmark Ph: (+45)

> 35327918

> ~~~~~~~~~~ - (

[hidden email]) FAX: (+45)

> 35327907

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