Usage
sCompReorder(sMap, xdim = NULL, ydim = NULL, amplifier = NULL,
metric = c("none", "pearson", "spearman", "kendall", "euclidean",
"manhattan", "cos", "mi"), init = c("linear", "uniform", "sample"),
algorithm = c("sequential", "batch"), alphaType = c("invert", "linear",
"power"), neighKernel = c("gaussian", "bubble", "cutgaussian", "ep",
"gamma"))
Arguments
sMap
an object of class "sMap" or input data
frame/matrix
xdim
an integer specifying x-dimension of the
grid
ydim
an integer specifying y-dimension of the
grid
amplifier
an integer specifying the amplifier (3
by default) of the number of component planes. The
product of the component number and the amplifier
constitutes the number of rectangles in the sheet grid
metric
distance metric used to difine the
similarity between component planes. It can be "none",
which means directly using column-wise vectors of
codebook/data matrix. Otherwise, first calculate the
covariance matrix from the codebook/data matrix. The
distance metric used for calculating the covariance
matrix between component planes can be: "pearson" for
pearson correlation, "spearman" for spearman rank
correlation, "kendall" for kendall tau rank correlation,
"euclidean" for euclidean distance, "manhattan" for
cityblock distance, "cos" for cosine similarity, "mi" for
mutual information. See sDistance for
details init
an initialisation method. It can be one of
"uniform", "sample" and "linear" initialisation methods
algorithm
the training algorithm. It can be one of
"sequential" and "batch" algorithm. By default, it uses
'sequential' algorithm. If the input data contains a
large number of samples but not a great amount of zero
entries, then it is reasonable to use 'batch' algorithm
for its fast computations (probably also without the
compromise of accuracy)
alphaType
the alpha type. It can be one of
"invert", "linear" and "power" alpha types
neighKernel
the training neighbor kernel. It can
be one of "gaussian", "bubble", "cutgaussian", "ep" and
"gamma" kernels