Either a random assignment of k approximately equal size clusters or a FastMap-like algorithm that sequentially selects k distant ids from those that have more than the median number of observations. TPS fits to these ids are used as cluster centers for a starting group assignment. A user supplied starting assignment is also possible.
start_groups(k, data, starts, maxdf, conv, mccores = 1, verbose = FALSE)An integer vector corresponding to unique ids, giving group number
assignments.
For distant, each sequential selection takes an id that has the largest
minimum distance from smooth TPS fits (<= 5 deg) of previous selections.
The distance of an id to a single TPS is the median absolute error across
the id time points. Distance of an id to a set of TPS is the minimum of
the individual distances. We pick the id that has the maximum of such
a minimum of medians.
Number of clusters (groups).
Data.table with response measurements, one per observation.
Column names are id, time, response, group. Note that ids are assumed
sequential starting from 1. This affects expanding group numbers to ids.
Type of start groups generated. See clustra.
Fitting parameters. See trajectories.
Fitting parameters. See trajectories.
See trajectories.
Turn on more output for debugging. Values 0, 1, 2, 3 add more output. 2 and 3 produce graphs during iterations - use carefully!