This is correct. The function, trajectory.analysis, compares multi-point trajectories; therefore, age would need to be modeled as a categorical variable (factor). If one wishes to have age (or similar variable) in the model as a continuous variable, then advanced.procD.lm would be more appropriate. This function allows comparisons of slopes among groups. The two functions are similar, in that they allow evaluation of the length and direction of group trajectories, although the trajectories in advancad.procD.lm are vectors. One can consider non-linear trajectories in trajectory.analysis.
Hope that helps! 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 Aug 24, 2015, at 12:11 AM, lv xiao <[email protected]<mailto:[email protected]>> wrote: In the botton line of page 53 of Quick Guide to Geomorph v2.1.6 regarding trajectory.analysis (Y ~ cov + A * B), A and B are called "factors", which seems to suggest that A and B are categorical variables. Continuous covariates could be included in the formula, but this is only optional. In contrast, it seems that there must be two categorical variables (A and B) appearing in the formula. Following this line of thought, I am wondering if is there the need to convert the continuous age variable (age) into a categorical variable (age_cat) before applying the trajectory analysis. I am not sure whether one should use trajectory.analysis(shape ~ group * age_cat ) or trajectory.analysis(shape ~ group * age). Best regards, Patrick On Monday, 24 August 2015 10:56:55 UTC+8, Emma Sherratt wrote: Dear Miranda, Using procD.lm is the correct function for what you want to do. Since you have just two groups it's a simple Procrustes Anova. Your implementation should be: procD.lm(shape~ age*group) This will give you: Effect of age; where significant means the shape scales allometrically Effect of group; where significant means the groups differ in intercept The interaction term of age and group to tell you if the two groups have the same slope (interaction term not significant) or the slopes differ (sig interaction term) Then from this you will be able to deduce whether the two groups follow the same allometric trajectory or not. But remember, you are dealing with multivariate regression here so there is no positive or negative allometry, since the "slope" is a multivariate vector in shape space. The same formula into trajectory.analysis should then lead you to where you were hoping to go with that. Emma On Monday, August 24, 2015, Karban, Miranda E <[email protected]<http://uiowa.edu/>> wrote: Hello morphometricians, I am relatively new to morphometrics, and I am attempting to assess ontogenetic trajectories from a longitudinal sample of growth study x-rays. My subjects are divided into 2 groups, and I would like to determine whether there are developmental differences in cranial shape between these groups. I have precise ages for each subject, so I hope to use age as a variable (following McNulty et al., 2006) rather than centroid size. >From what I gather from the literature, I can estimate ontogenetic >trajectories by regressing the Procrustes aligned shape coordinates onto the >independent variable of age. So far, I have attempted to do this in the >geomorph package in R using the procD.lm and the trajectory.analysis >functions. I am wondering if I am doing this correctly, or if there is a >better function to use. I have tried the following: lateral.gpa <- gpagen(vaultlandmarks) procD.lm(two.d.array(lateral.gpa$coords) ~ age, iter = 999) where “vaultlandmarks” refers to the 2D landmark and semi-landmark coordinates in my tps file, and “age” refers to a column in my metadata csv file which gives the age of each specimen to the nearest 1/10 of a year. This provides a sum-of-squared Procrustes distances, a mean square, and a highly significant p-value. I am not sure, however, how to compare the results I get from the 2 groups. When I try the trajectory.analysis function: lateral.gpa <- two.d.array(gpagen(vaultlandmarks)$coords) trajectory.analysis(lateral.gpa~age) I get the error message: “Error in trajectory.analysis(lateral.gpa ~ age) : X-matrix does not specify enough model factors (see help file).” Thank you for any advice or help you might provide. Best, Miranda Karban PhD Candidate, University of Iowa -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org<http://www.morphometrics.org/> To unsubscribe from this group and stop receiving emails from it, send an email to morphmet+unsubscribe@<mailto:[email protected]>morphometrics.org<mailto:[email protected]>. -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Emma Sherratt, PhD. Lecturer in Zoology, Zoology Division, School of Environmental and Rural Science, Room L112 Bldg C02, University of New England, Armidale, NSW, Australia, 2351 Tel: +61 2 6773 5041 email: [email protected].<http://une.edu.au/>au<http://une.edu.au/> Twitter: @DrEmSherratt Caecilians are legless amphibians... __ (\ .-. .-. /_") \\_/<smb://_/>/^\\_//^\\_// `"` `"` `"` learn more about them here: www.emmasherratt.com/caecilians<http://www.emmasherratt.com/caecilians> -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org<http://www.morphometrics.org/> 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 To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
