plotAllometry(A, sz, groups = NULL,
method = c("CAC", "RegScore", "PredLine"),
warpgrids = TRUE, label = FALSE)gpagen]. The abscissa of the plot is
log(centroid size) while the ordinate represents shape.
Three complementary approaches can be implemented to
visualize allometry. If "method=CAC" (the default) the
function calculates the common allometric component of
the shape data, which is an estimate of the average
allometric trend within groups (Mitteroecker et al.
2004). If "method=RegScore" the function calculates shape
scores from the regression of shape on size, and plots
these versus size (Drake and Klingenberg 2008). For a
single group, these shape scores are mathematically
identical to the CAC (Adams et al. 2012). If
"method=PredLine" the function calculates predicted
values from a regression of shape on size, and plots the
first principal component of the predicted values versus
size as a stylized graphic of the allometric trend (Adams
and Nistri 2010). For all methods, both centroid size and
allometry scores are returned. Optionally, deformation
grids can be requested, which display the shape of the
smallest and largest specimens relative to the average
specimen (using 'warpgrid=T' or 'warpgrid=F'). Finally,
if groups are provided, the above approaches are
implemented while accounting for within-group patterns of
covariation (see references for explanation).data(rats)
Y.gpa<-gpagen(ratland) #GPA-alignment
#Using CAC for plot
plotAllometry(Y.gpa$coords,Y.gpa$Csize,method="CAC")
#Using Regression Scores for plot
plotAllometry(Y.gpa$coords,Y.gpa$Csize,method="RegScore")
#Using predicted allometry curve for plot
plotAllometry(Y.gpa$coords,Y.gpa$Csize,method="PredLine")Run the code above in your browser using DataLab