Description Usage Arguments Value Author(s) References Examples
View source: R/Partition_Map.R
Creates partitions based on partial moment quadrant means, iteratively assigning identifications to observations based on those quadrants (unsupervised partitional and hierarchial clustering method). Basis for correlation NNS.cor, dependence NNS.dep, regression NNS.reg routines.
1 2 
x 
a numeric vector. 
y 
a numeric vector with compatible dimsensions to 
Voronoi 
logical; 
type 

order 
integer; Number of partial moment quadrants to be generated. 
max.obs.req 
integer; (4 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 
min.obs.stop 
logical; 
noise.reduction 
the method of determing regression points options: ("mean", "median", "mode", "off"); 
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.
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  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

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