trajectory.analysis(f1, data = NULL, estimate.traj = TRUE,
traj.pts = NULL, iter = 999, verbose = FALSE)two.d.array can be used to obtain a two-dimensional data matrix from a 3D array of landmark
coordinates. It is assumed that the order of the specimens 'Y' matches the order of specimens in 'X'.
There are two primary modes of analysis through this function. If "estimate.traj=TRUE" the function
estimates shape trajectories using the least-squares means for groups, based on a two-factor model
(e.g., Y~A+B+A:B). Under this implementation, the last factor in 'X' must be the interaction term, and
the preceding two factors must be the effects of interest. Covariates may be included in 'X', and must
precede the factors of interest (e.g., Y~cov+A*B). In this implementation, 'Y' contains a matrix of landmark
coordinates. It is assumed that the landmarks have previously been aligned using Generalized Procrustes
Analysis (GPA) [e.g., with gpagen].
If "estimate.traj=FALSE" the trajectories are assembled directly from the set of shapes provided in 'Y'.
With this implementation, the user must specify the number of shapes that comprise each trajectory. This
approach is useful when the set of shapes forming each trajectory have been quantified directly
(e.g., when motion paths are compared: see Adams and Cerney 2007). With this implementation, variation in
trajectory size, shape, and orientation are evaluated for each term in 'X'.(see Adams and Cerney 2007).
Currently only single-factor analyses are supportd with this method.
Once the function has performed the analysis, it generates a plot of the trajectories as visualized in the
space of principal components (PC1 vs. PC2). The first point in each trajectory is displayed as white, the
last point is black, and any middle points on the trajectories are in gray. The colors of trajectories follow
the order in which they are found in the dataset, using R's standard color palette: black, red, green3,
blue, cyan, magenta, yellow, and gray.#1: Estimate trajectories from LS means in 2-factor model
data(plethodon)
Y.gpa<-two.d.array(gpagen(plethodon$land)$coords)
trajectory.analysis(Y.gpa~plethodon$species*plethodon$site,iter=15)
# Retaining random values (first sets are always observed)
tra <- trajectory.analysis(Y.gpa~plethodon$species*plethodon$site,iter=15, verbose = TRUE)
tra$anova.table
tra$Random.values
#2: Compare motion trajectories
data(motionpaths)
#Motion paths represented by 5 time points per motion
trajectory.analysis(motionpaths$trajectories~motionpaths$groups,
estimate.traj=FALSE, traj.pts=5,iter=15)
trajectory.analysis(motionpaths$trajectories~motionpaths$groups,
estimate.traj=FALSE, traj.pts=5,iter=15, verbose=TRUE)Run the code above in your browser using DataLab