bdm.dMap {bigMap} | R Documentation |
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)
bdm |
A bdm instance as generated by |
threads |
The number of parallel threads (in principle only limited by hardware resources, |
type |
The type of cluster: 'SOCK' (default) for intra-node parallelization, 'MPI' for inter-node parallelization ( |
data |
A vector of discret covariates or class labels. The covariate values can be of any factorizable type. By default ( |
layer |
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
bdm.example()
## Not run:
exMap <- bdm.dMap(exMap, threads = 4)
## End(Not run)