NNS (version 0.5.6)

PM.matrix: 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 = "mean", variable, pop.adj = FALSE)

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 = "mean") (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.

Value

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

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

# NOT RUN {
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, target = "mean", variable = A)

## 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)

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

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