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NNS Partition Map

Source: R/Partition_Map.R
NNS.part.Rd

Creates partitions based on partial moment quadrant centroids, iteratively assigning identifications to observations based on those quadrants (unsupervised partitional and hierarchical clustering method). Basis for correlation, dependence NNS.dep, regression NNS.reg routines.

Usage

NNS.part(
  x,
  y,
  Voronoi = FALSE,
  type = NULL,
  order = NULL,
  obs.req = 8,
  min.obs.stop = TRUE,
  noise.reduction = "off"
)

Arguments

x

a numeric vector.

y

a numeric vector with compatible dimensions to x.

Voronoi

logical; FALSE (default) Displays a Voronoi type diagram using partial moment quadrants.

type

NULL (default) Controls the partitioning basis. Set to (type = "XONLY") for X-axis based partitioning. Defaults to NULL for both X and Y-axis partitioning.

order

integer; Number of partial moment quadrants to be generated. (order = "max") will institute a perfect fit.

obs.req

integer; (8 default) Required observations per cluster where quadrants will not be further partitioned if observations are not greater than the entered value. Reduces minimum number of necessary observations in a quadrant to 1 when (obs.req = 1).

min.obs.stop

logical; TRUE (default) Stopping condition where quadrants will not be further partitioned if a single cluster contains less than the entered value of obs.req.

noise.reduction

the method of determining regression points options for the dependent variable y: ("mean", "median", "mode", "off"); (noise.reduction = "mean") uses means for partitions. (noise.reduction = "median") uses medians instead of means for partitions, while (noise.reduction = "mode") uses modes instead of means for partitions. Defaults to (noise.reduction = "off") where an overall central tendency measure is used, which is the default for the independent variable x.

Value

Returns:

  • "dt" a data.table of x and y observations with their partition assignment "quadrant" in the 3rd column and their prior partition assignment "prior.quadrant" in the 4th column.

  • "regression.points" the data.table of regression points for that given (order = ...).

  • "order" the order of the final partition given "min.obs.stop" stopping condition.

Note

min.obs.stop = FALSE will not generate regression points due to unequal partitioning of quadrants from individual cluster observations.

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)

Author

Fred Viole, OVVO Financial Systems

Examples

if (FALSE) { # \dontrun{
set.seed(123)
x <- rnorm(100) ; y <- rnorm(100)
NNS.part(x, y)

## Data.table of observations and partitions
NNS.part(x, y, order = 1)$dt

## Regression points
NNS.part(x, y, order = 1)$regression.points

## Voronoi style plot
NNS.part(x, y, Voronoi = TRUE)

## Examine final counts by quadrant
DT <- NNS.part(x, y)$dt
DT[ , counts := .N, by = quadrant]
DT
} # }