sWriteData {supraHex} | R Documentation |
sWriteData
is supposed to write out the best-matching hexagons
and/or cluster bases in terms of data.
sWriteData(sMap, data, sBase = NULL, filename = NULL, keep.data = FALSE)
sMap |
an object of class "sMap" or a codebook matrix |
data |
a data frame or matrix of input data |
sBase |
an object of class "sBase" |
filename |
a character string naming a filename |
keep.data |
logical to indicate whether or not to also write out the input data. By default, it sets to false for not keeping it. It is highly expensive to keep the large data sets |
a data frame with following components:
ID
: ID for data. It inherits the rownames of data (if
exists). Otherwise, it is sequential integer values starting with 1 and
ending with dlen, the total number of rows of the input data
Hexagon_index
: the index for best-matching hexagons
Cluster_base
: optional, it is only appended when sBase is
given. It stores the cluster memberships/bases
data
: optional, it is only appended when keep.data is
true
If "filename" is not NULL, a tab-delimited text file will be also written out. If "sBase" is not NULL and comes from the "sMap" partition, then cluster bases are also appended. if "keep.data" is true, the data will be part of output.
# 1) generate an iid normal random matrix of 100x10 data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10) # 2) get trained using by default setup sMap <- sPipeline(data=data) # 3) write data's BMH hitting the trained map output <- sWriteData(sMap=sMap, data=data, filename="sData_output.txt") # 4) partition the grid map into cluster bases sBase <- sDmatCluster(sMap=sMap, which_neigh=1, distMeasure="median", clusterLinkage="average") # 5) write data's BMH and cluster bases output <- sWriteData(sMap=sMap, data=data, sBase=sBase, filename="sData_base_output.txt")