PM.matrix | R Documentation |
This function generates a co-partial moment matrix for the specified co-partial moment.
PM.matrix(LPM_degree, UPM_degree, target, variable, pop_adj)
LPM_degree |
integer; Degree for |
UPM_degree |
integer; Degree for |
target |
numeric; Typically the mean of Variable X for classical statistics equivalences, but does not have to be. (Vectorized) |
variable |
a numeric matrix or data.frame. |
pop_adj |
logical; |
Matrix of partial moment quadrant values (CUPM, DUPM, DLPM, CLPM), and overall covariance matrix. Uncalled quadrants will return a matrix of zeros.
For divergent asymmetical "D.LPM" and "D.UPM"
matrices, matrix is D.LPM(column,row,...)
.
Fred Viole, OVVO Financial Systems
Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)
Viole, F. (2017) "Bayes' Theorem From Partial Moments" \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2139/ssrn.3457377")}
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, target = colMeans(A), pop_adj = TRUE)
## 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, pop_adj = TRUE)
## Calling Individual Partial Moment Quadrants
cov.mtx <- PM.matrix(LPM_degree = 1, UPM_degree = 1, variable = A, target = colMeans(A),
pop_adj = TRUE)
cov.mtx$cupm
## Full covariance matrix
cov.mtx$cov.matrix
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