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