Dear Brenna, There is a lot to digest here, so apologies up front if I miss something. Also, I’ll keep my response general for the benefit of our colleagues. We can work out specific issues with your data and your analyses via direct email communication.
First, you have two sets of shape variables that you present so it is difficult to compare your different results and understand exactly the problem, but I see a couple of things. I’m surprised you did not get an error message, as we set traps to avoid the type of nested analysis you were trying to perform. Currently, nested.update is not that sophisticated to handle a structure like ~ a/b/c. You eluded the trap, somehow (I’ll have to look into this), but if c is nested within b and b is nested with a, and one wants to adjust F values such that F for b = MSb/MSc and F for a = MAa/MSb, the procedure should be to use a recursive strategy with nested.update, to update the fit first by ~b/c, then use that update and update it again with ~a/b. I’m not sure what to tell you about the results because I am surprised you got results. Second, the reason for the same results in your second and third example is that bout/rep is the same the error. In this case, nested.update will not accomplish anything, other than identify that there are no residuals after you account for your nested effect. Third, your centroid size (near) invariance appears to have resulted from performing GPA on a subset of data that already had GPA performed. You found a significant shape-size association despite what appeared to be little variation, but this might just be a scaling issue. For example, multiply the centroid size values by 10000 and re-run the analysis. The SS and MS will change, and the new variable will seem less invariant, but I suspect the F-value and P-value will not change. Good luck and feel free to send me your data-specific questions! Mike Michael Collyer Associate Professor Biostatistics Department of Biology Western Kentucky University 1906 College Heights Blvd. #11080 Bowling Green, KY 42101-1080 Phone: 270-745-8765; Fax: 270-745-6856 Email: [email protected]<mailto:[email protected]> On Mar 4, 2016, at 2:45 PM, Brenna Hays <[email protected]<mailto:[email protected]>> wrote: Hi All, I am trying to perform a nested ANOVA in R to test for repeatability and subjectivity of landmark placements on three randomly chosen images. I have landmarked each image 10 times, in 2 separate bouts (a few months apart). This is my result from incorporating the entire data set into a nested ANOVA: #nested ANOVA error <- procD.lm(f1=ALLlp.shape~ind/bout/rep, iter=999, RRPP=TRUE, data=NULL) error <- nested.update(error, ~ind/bout/rep) summary(error) Call: procD.lm(f1 = ALLlp.shape ~ ind/bout/rep, iter = 999, RRPP = TRUE, data = NULL) Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 1000 Permutations Df SS MS Rsq F Z Pr(>F) ind 2 0.71265 0.35632 0.98767 1410.6169 19.2446 0.001 ** ind:bout 1 0.00276 0.00276 0.00383 10.9405 13.4952 0.001 ** ind:bout:rep 36 0.00108 0.00003 0.00150 0.1191 0.2891 1.000 Residuals 20 0.00505 0.00025 Total 59 0.72155 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 I was concerned, however, that these results are comparing between individuals (images) which I do not want, since all three images are of different individuals. Really, I just want to test variance between bouts and reps, so I performed separate nested ANOVAs for each individual. Here is the result for one of the individuals (note: it will not let me put the ind factor in since for each separate test there is only one category (1 image)): > error = procD.lm(f1=CochaClp.shape~bout/rep, iter=999, RRPP=TRUE, data=NULL) > error <- nested.update(error, ~bout/rep) > summary(error) Call: procD.lm(f1 = CochaClp.shape ~ bout/rep, iter = 999, RRPP = TRUE, data = NULL) Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 1000 Permutations *** F values, Z scores, and P values updated for nested effects Df SS MS Rsq F Z Pr(>F) bout 1 0.00238108 0.00238108 0.75651 55.925 9.0398 0.001 ** bout:rep 18 0.00076637 0.00004258 0.24349 1.0534 0.001 ** Residuals 0 0.00000000 Total 19 0.00314745 I also performed an ANOVA on just the bout factor to see. I don't know if the residual sum of squares and mean squares should match the bout:rep numbers above, but it doesn't seem right to me. > errorb = procD.lm(CochaClp.shape~bout, iter=999, RRPP=TRUE) > summary(errorb) Call: procD.lm(f1 = CochaClp.shape ~ bout, iter = 999, RRPP = TRUE) Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 1000 Permutations Df SS MS Rsq F Z Pr(>F) bout 1 0.00238108 0.00238108 0.75651 55.925 9.5151 0.001 ** Residuals 18 0.00076637 0.00004258 Total 19 0.00314745 Additionally, on a simpler note, I performed an ANOVA testing the significance of centroid size on shape. The results here do not make sense to me either - it states a significant variance but the variance of the centroid sizes is so small. > csize <- centroid.size(CochaClp.shape) > csize [1] 1.000062 1.000067 1.000064 1.000068 1.000059 1.000075 1.000071 1.000074 1.000079 1.000066 [11] 1.000070 1.000122 1.000065 1.000093 1.000071 1.000098 1.000117 1.000097 1.000072 1.000083 > var(csize) [1] 3.170611e-10 Call: procD.lm(f1 = CochaClp.shape ~ log(csize), iter = 999, seed = NULL, RRPP = TRUE, int.first = FALSE, data = NULL) Type I (Sequential) Sums of Squares and Cross-products Randomized Residual Permutation Procedure Used 1000 Permutations Df SS MS Rsq F Z Pr(>F) log(csize) 1 0.00098216 0.00098216 0.31205 8.1646 4.1957 0.002 ** Residuals 18 0.00216529 0.00012029 Total 19 0.00314745 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 I believe, as a whole, I am either doing something wrong in my coding with procD.lm, or interpreting it incorrectly. Any input or suggestions would be extremely helpful. Thank you! Brenna -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org<http://www.morphometrics.org/> --- 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]<mailto:[email protected]>. -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org --- 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].
