# sDmat: Function to calculate distance matrix in high-dimensional... In supraHex: supraHex: a supra-hexagonal map for analysing tabular omics data

## Description

`sDmat` is supposed to calculate distance (measured in high-dimensional input space) to neighbors (defined by based on 2D output space) for each of hexagons/rectangles

## Usage

 ```1 2``` ```sDmat(sMap, which_neigh = 1, distMeasure = c("median", "mean", "min", "max")) ```

## Arguments

 `sMap` an object of class "sMap" `which_neigh` which neighbors in 2D output space are used for the calculation. By default, it sets to "1" for direct neighbors, and "2" for neighbors within neighbors no more than 2, and so on `distMeasure` distance measure used to calculate distances in high-dimensional input space

## Value

• `dMat`: a vector with the length of nHex. It stores the distance a hexaon/rectangle is away from its output-space-defined neighbors in high-dimensional input space

## Note

"which_neigh" is defined in output 2D space, but "distMeasure" is defined in high-dimensional input space

`sNeighAny`
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# 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) calculate "median" distances in INPUT space to different neighbors in 2D OUTPUT space # 3a) using direct neighbors in 2D OUTPUT space dMat <- sDmat(sMap=sMap, which_neigh=1, distMeasure="median") # 3b) using no more than 2-topological neighbors in 2D OUTPUT space # dMat <- sDmat(sMap=sMap, which_neigh=2, distMeasure="median") ```