My sample size is 147 not either 9 or 7 in any case. I agree that the
computer displays these numbers! don't know why?
Nevertheless, in this analysis the number of specimens (dependent shape
variable) is 147, obviously, with the equal number of observations for each
independent variables (here,
Mike,
That's amazing, you nailed it, finally. Great!
Yes, I didn’t have all levels of stages that match all levels of Locations,
and because of this, I am getting such error messages. Thanks a lot indeed,
for educating and enlightening me, on these aspects.
I do have some other factors with
Dear colleagues,
I'm trying to figure out how to break down shape differences onto
individual PC axes. I have a morphospace where PC1 explains 60% of shape
variation and PC2 explains 20% of variation. Two subsets of samples of
particular interest do not differ much along PC1, but differs
Dear colleagues,
I'm trying to figure out how to break down shape differences onto
individual PC axes. I have a morphospace where PC1 explains 60% of shape
variation and PC2 explains 20% of variation. Two subsets of samples of
particular interest do not differ much along PC1, but differs
Dear all,
Please see some of our recent papers, in case you share an interest in how GMM
can benefit face research :)
Windhager, Bookstein, Mueller, Zunder, Kirchengast, Schaefer 2018 Calibrating
facial morphs for use as stimuli in biological studies of social perception.
Sci Rep
Full text at
Mahen,
I think the issue is because you might not have all levels of Location matched
with all Levels of Stages. It appears you have 5 stages and 2 locations. For
the model that worked, you will notice 5 rows in your ANOVA table. In the one
that didn’t, it was attempting to create a table
Dear Dr. Hu,
Let me begin by restating how I understand the question: You have completed
a PCA on a morphological data set in which there are two subsets of
interest. Now you would like to decompose the difference between the two
subsets into differences along individual PCs. Here is my two cents
Yes, the PC axes are “comparable”. I think the best way to think about what a
PCA does is to interpret it as a projection of a multidimensional space down to
a low dimensional space that captures as much of the overall variation as
possible. The first axis is somewhat special because it
Dear James,
Thanks for the reply. Yes I have completed a PCA on a GM dataset with 11
landmarks, and you got it exactly right that I'm trying to decompose shape
differences onto individual PCs.
The reason I was hesitating to do the vector projection is that I'm not
sure if PC scores on
