bdm.dMap {bigMap}R Documentation

Class density maps


Compute the class density maps of a set of classes on the embedding grid. This function returns a fuzzy mapping of the set of classes on the grid cells. The classes can be whatever set of classes of interest and must be given as a vector of point-wise discrete labels (either numeric, string or factor).


bdm.dMap(bdm, threads = 2, type = "SOCK", data = NULL, layer = 1)



A bdm instance as generated by bdm.init().


The number of parallel threads (in principle only limited by hardware resources, i.e. number of cores and available memory)


The type of cluster: 'SOCK' (default) for intra-node parallelization, 'MPI' for inter-node parallelization (message passing interface parallel environment).


A vector of discret covariates or class labels. The covariate values can be of any factorizable type. By default (data=NULL) the function computes the density maps based on the clustering labels (i.e. equivalent to data=bdm.labels(bdm))


The number of the t-SNE layer (1 by default).


bdm.dMap() computes the join distribution P(V=v_{i},C=c_{j}) where V={v_{1},\dots,v_{l}} is the discrete covariate and C={c_{1},\dots, c_{g}} are the grid cells of the paKDE raster. That is, this function recomputes the paKDE but keeping track of the covariate (or class) label of each data-point. This results in a fuzzy distribution of the covariate (class) at each cell.

Usually, figuring out the join distribution P(V=v_{i},C=c_{j}) entails an intensive computation. Thus bdm.dMap() performs the computation and stores the result in a dedicated element named $dMap. Afterwards the class density maps can be visualized with the bdm.dMap.plot() function.


A copy of the input bdm instance with element $dMap, a matrix with a soft clustering of the grid cells.


# --- load example dataset
## Not run: 
exMap <- bdm.dMap(exMap, threads = 4)

## End(Not run)

[Package bigMap version 2.3.1 Index]