View source: R/Partition_Map.R
NNS.part  R Documentation 
Creates partitions based on partial moment quadrant centroids, iteratively assigning identifications to observations based on those quadrants (unsupervised partitional and hierarchial clustering method). Basis for correlation, dependence NNS.dep, regression NNS.reg routines.
NNS.part( x, y, Voronoi = FALSE, type = NULL, order = NULL, obs.req = 8, min.obs.stop = TRUE, noise.reduction = "off" )
x 
a numeric vector. 
y 
a numeric vector with compatible dimensions to 
Voronoi 
logical; 
type 

order 
integer; Number of partial moment quadrants to be generated. 
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 
min.obs.stop 
logical; 
noise.reduction 
the method of determining regression points options for the dependent variable 
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.
min.obs.stop = FALSE
will not generate regression points due to unequal partitioning of quadrants from individual cluster observations.
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp
## Not run: 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 ## End(Not run)
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