Dear All,
beside the excellent review by Carmelo, I suggest a few other papers
on ME in geometric morphometrics:
 Arnqvist, G., Martensson, T. Measurement error in geometric
morphometrics: empirical strategies to assess and reduce its impact on
measures of shape. Acta Zoologica Academiae Scientiarum Hungaricae,
1998, 44: 73–96. (A bit outdated but still wonderfully accurate in how
they explain different sources of ME).
 Klingenberg, C.P., Barluenga, M., Meyer, A. Shape Analysis of
Symmetric Structures: Quantifying Variation Among Individuals and
Asymmetry. Evolution, 2002, 56: 1909–1920. (From where most of us have
borrowed the protocol for assessing ME).
 Viscosi, V., Cardini, A. Leaf Morphology, Taxonomy and Geometric
Morphometrics: A Simplified Protocol for Beginners. PLoS ONE, 2011, 6:
e25630.
 Galimberti, F., Sanvito, S., Vinesi, M.C., Cardini, A. “Nose-metrics”
of wild southern elephant seal (Mirounga leonina) males using image
analysis and geometric morphometrics. Journal of Zoological
Systematics and Evolutionary Research, 2019, 57: 710–720.

There's also another one I like, by the Viennese morphometricians (in
a paper on human mandibles, or teeth, symmetric and asymmetric
variation, if I remember well), but I can't find it now.


In general, the idea is that differences among individuals (averaged
replicates) in a representative sample should be larger than
differences between replicates of the same individual (the estimate of
ME). This is what is tested by 'individual' in the Procrustes ANOVA in
MorphoJ. It might be important to control for main effects in the
analysis. For instance, by including species and sex before individual
in the hierarchical analysis, I 'statistically remove' (with some
assumptions) the average effect of these factors before comparing
individual variation to ME, which makes the test more conservative (NB
whether this is OK or not it depends on the question one is asking in
her/his study).
For shape data, even if the P value of individual vs residual is
significant, I would not conclude that ME is negligible for sure. I'd
check that the individual Rsq is much larger than the ME (residual)
Rsq and also that shape distances between replicates of the same
individual are smaller than distances among different individuals (if
this is true, replicates should cluster 'within individual' in a UPGMA
phenogram). Then, I feel a bit more confident that ME might be
negligible.

If ME is large, it may happen that its Rsq is larger than the
individual Rsq (or, which is the same ME SSQ > individual SSQ). For
the F ratio, however, one should look at the mean SSQ, which take df
into account. From the MSSQ, one computes F.
The F ratio in MorphoJ employs an isotropic model but, with large
samples (relative to the number of variables), the software also
provides P values using Pillai, that does not depend (if I recall
well!) on an isotropic model. That N is large and the sample
representative is crucial if one is using a subsample in the
assessment of ME to avoid replicate measurements of all individuals,
which would be better but might take too long if one has hundreds or
thousands individuals.
In R, I generally use adonis that employs an F test (same as in
MorphoJ, for a simple design) but uses permutations instead of
parametric tests. The use of permutations was also suggested as
desirable in Klingenberg et al., 2002. Other packages I suspect might
do something similar, although maybe using different permutational
approaches. I am sure it is explained in their help files.

Cheers

Andrea

On 03/11/2022, ying yi <[email protected]> wrote:
> Dear all,
>      I used the “procD.lm” function in the geomorph package to test the
> measurement error. I was surprised to find that the within-groups ANOVA sum
>
> of squares  I got was greater than the among-groups ANOVA sum of squares. I
>
> wonder if something went wrong. What does it mean for “procD.lm” function
> to get an F value <1?
> I would be very happy if someone could help me.
> Yours,
> Sam
>
> References are as follows:
>
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