Usage
spe( coord, rcutpercent = 1, maxdist = 0, nobs = 0, ndim = 0, edim, lambda0 = 2.0, lambda1 = 0.01, nstep = 1e6, ncycle = 100, evalstress=FALSE, sampledist=TRUE, samplesize = 1e6)
Arguments
coord
This should be a matrix with number of rows equal to the number of observations
and number of columns equal to the input dimension. A data.frame may also be supplied
and it will be converted to a matrix (so all names will be lost)
rcutpercent
This is the percentage of the maximum distance (as determined by probability sampling)
that will be used as the neighborhood radius. Setting rcutpercent to a value greater than 1 effectively sets it to
infinity.
maxdist
If you have alread calculated a mxaimum distance then you can supply it and
probability sampling will not be carried out to obtain a maximum distance. The default is
to carry out sampling. By setting maxdist to a non zero value sampling will not be
carried out (even if sampledist=TRUE)
nobs
The number of observations. If it is not specified nobs will be taken as nrow(coord)
ndim
The number of input dimensions. If not specified it will be taken as ncol(coord)
edim
The number of dimensions to embed in
lambda0
The starting value of the learning parameter
lambda1
The ending value of the learning parameter
nstep
The number of refinement steps
ncycle
The number of cycles to carry out refinement for
evalstress
If TRUE the function will evaluate the Sammon stress on the final embedding
sampledist
If TRUE an approximation to the maximum distance in the input dimensions will
be obtained via probability sampling
samplesize
The number of iterations for probability sampling. For a dataset of 6070 observations
there will be 6070x6069/2 pairwise distances. The default value gives a close approximation and runs
fast. If you want a bettr approximation 1e7 is a good value. YMMV