Dear Sam, You should consider mean sums of squares rather than rough sums of squares.... (F stat is computed on the ratio of these not on SS directly). If you design was replicating measurement per individual, and if mean sums of squares for error is greater than mean sums of squares for individual variation, it means that the expected trace for individual variances is negative; and that your percentage of measurement error (or intraclass coefficient) is likely greater than what you are measuring. You can try to provide the percentage of measurement error by computing expected traces... I think some of us have been using that strategy in the past and this can give you an idea (see for instance how I made it in the hystrix volume script; it is referring to Yzerinac et al in syst biology, 1992)..
let's call m: number of replicates per individual SSi: trace for inter-individual sum of squares and crossproduct SSe: trace for intra-individual sum of squares and crossproduct n: numer of individuals Your averaged sums of squares are obtained: MSSi<-SSi/(n-1) MSSe<-SSe/(nm - n) your percentage of measurement error should be: MSSe / [MSSe+ (MSSi- MSSe)/m] In your case, F being greater than 1, then MSSe> MSSi and the last denominator will exceed the numerator; so your percentage of measurement error is higher than 100%. You are then trying to measure something that is not measurable because you do not have enough precision. Individual shape variation is sometime very subtle and can not be estimated, but maybe you made a super big error measurement for one or two individuals...To avoid that, you can try to increase replicates, or to see whether there is not one or a set of individuals on which error was extreme and correct your digitization. If you are lucky, the percentage will become < 100%, and then it will mean that you will be able to measure something from your data. Julien > De: "ying yi" <[email protected]> > À: "Morphmet" <[email protected]> > Envoyé: Jeudi 3 Novembre 2022 15:19:18 > Objet: [MORPHMET2] Measurement error in geometric morphometrics > 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: > -- > You received this message because you are subscribed to the Google Groups > "Morphmet" group. > To unsubscribe from this group and stop receiving emails from it, send an > email > to [ mailto:[email protected] | > [email protected] ] . > To view this discussion on the web visit [ > https://groups.google.com/d/msgid/morphmet2/06065841-c42e-4a58-a5d3-a96eb3c5787dn%40googlegroups.com?utm_medium=email&utm_source=footer > | > https://groups.google.com/d/msgid/morphmet2/06065841-c42e-4a58-a5d3-a96eb3c5787dn%40googlegroups.com > ] . -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/morphmet2/518085623.3329405.1667531417996.JavaMail.zimbra%40umontpellier.fr.
