PM.matrix {NNS}R Documentation

Partial Moment Matrix

Description

This function generates a co-partial moment matrix for the specified co-partial moment.

Usage

PM.matrix(
  LPM.degree,
  UPM.degree,
  target = NULL,
  variable,
  pop.adj = FALSE,
  ncores = NULL
)

Arguments

LPM.degree

integer; Degree for variable below target deviations. (degree = 0) is frequency, (degree = 1) is area.

UPM.degree

integer; Degree for variable above target deviations. (degree = 0) is frequency, (degree = 1) is area.

target

numeric; Typically the mean of Variable X for classical statistics equivalences, but does not have to be. (Vectorized) (target = NULL) (default) will set the target as the mean of every variable.

variable

a numeric matrix or data.frame.

pop.adj

logical; FALSE (default) Adjusts the sample co-partial moment matrices for population statistics.

ncores

integer; value specifying the number of cores to be used in the parallelized procedure. If NULL (default), the number of cores to be used is equal to the number of cores of the machine - 1.

Value

Matrix of partial moment quadrant values (CUPM, DUPM, DLPM, CLPM), and overall covariance matrix. Uncalled quadrants will return a matrix of zeros.

Note

For divergent asymmetical "D.LPM" and "D.UPM" matrices, matrix is D.LPM(column,row,...).

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp

Viole, F. (2017) "Bayes' Theorem From Partial Moments" https://www.ssrn.com/abstract=3457377

Examples

set.seed(123)
x <- rnorm(100) ; y <- rnorm(100) ; z <- rnorm(100)
A <- cbind(x,y,z)
PM.matrix(LPM.degree = 1, UPM.degree = 1, variable = A, ncores = 1)

## Use of vectorized numeric targets (target_x, target_y, target_z)
PM.matrix(LPM.degree = 1, UPM.degree = 1, target = c(0, 0.15, .25), variable = A, ncores = 1)

## Calling Individual Partial Moment Quadrants
cov.mtx <- PM.matrix(LPM.degree = 1, UPM.degree = 1, variable = A, ncores = 1)
cov.mtx$cupm

## Full covariance matrix
cov.mtx$cov.matrix

[Package NNS version 0.8.70 Index]