R/RcppExports.R

In NNS: Nonlinear Nonparametric Statistics

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#' Lower Partial Moment
#'
#' This function generates a univariate lower partial moment for any degree or target.
#'
#' @param degree integer; \code{(degree = 0)} is frequency, \code{(degree = 1)} is area.
#' @param target numeric; Typically set to mean, but does not have to be. (Vectorized)
#' @param variable a numeric vector.  \link{data.frame} or \link{list} type objects are not permissible.
#' @return LPM of variable
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100)
#' LPM(0, mean(x), x)
#' @export
LPM <- function(degree, target, variable) {
    .Call(`_NNS_LPM_RCPP`, degree, target, variable)
}

#' Upper Partial Moment
#'
#' This function generates a univariate upper partial moment for any degree or target.
#' @param degree integer; \code{(degree = 0)} is frequency, \code{(degree = 1)} is area.
#' @param target numeric; Typically set to mean, but does not have to be. (Vectorized)
#' @param variable a numeric vector.   \link{data.frame} or \link{list} type objects are not permissible.
#' @return UPM of variable
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100)
#' UPM(0, mean(x), x)
#' @export
UPM <- function(degree, target, variable) {
    .Call(`_NNS_UPM_RCPP`, degree, target, variable)
}

#' Lower Partial Moment RATIO
#'
#' This function generates a standardized univariate lower partial moment for any degree or target.
#' @param degree integer; \code{(degree = 0)} is frequency, \code{(degree = 1)} is area.
#' @param target numeric; Typically set to mean, but does not have to be. (Vectorized)
#' @param variable a numeric vector.
#' @return Standardized LPM of variable
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @references Viole, F. (2017) "Continuous CDFs and ANOVA with NNS"
#' \url{https://www.ssrn.com/abstract=3007373}
#' @examples
#' set.seed(123)
#' x <- rnorm(100)
#' LPM.ratio(0, mean(x), x)
#'
#' \dontrun{
#' ## Empirical CDF (degree = 0)
#' lpm_cdf <- LPM.ratio(0, sort(x), x)
#' plot(sort(x), lpm_cdf)
#'
#' ## Continuous CDF (degree = 1)
#' lpm_cdf_1 <- LPM.ratio(1, sort(x), x)
#' plot(sort(x), lpm_cdf_1)
#'
#' ## Joint CDF
#' x <- rnorm(5000) ; y <- rnorm(5000)
#' plot3d(x, y, Co.LPM(0, sort(x), sort(y), x, y), col = "blue", xlab = "X", ylab = "Y",
#' zlab = "Probability", box = FALSE)
#' }
#' @export
LPM.ratio <- function(degree, target, variable) {
    .Call(`_NNS_LPM_ratio_RCPP`, degree, target, variable)
}

#' Upper Partial Moment RATIO
#'
#' This function generates a standardized univariate upper partial moment for any degree or target.
#' @param degree integer; \code{(degree = 0)} is frequency, \code{(degree = 1)} is area.
#' @param target numeric; Typically set to mean, but does not have to be. (Vectorized)
#' @param variable a numeric vector.
#' @return Standardized UPM of variable
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100)
#' UPM.ratio(0, mean(x), x)
#'
#' ## Joint Upper CDF
#' \dontrun{
#' x <- rnorm(5000) ; y <- rnorm(5000)
#' plot3d(x, y, Co.UPM(0, sort(x), sort(y), x, y), col = "blue", xlab = "X", ylab = "Y",
#' zlab = "Probability", box = FALSE)
#' }
#' @export
UPM.ratio <- function(degree, target, variable) {
    .Call(`_NNS_UPM_ratio_RCPP`, degree, target, variable)
}

#' Co-Lower Partial Moment
#' (Lower Left Quadrant 4)
#'
#' This function generates a co-lower partial moment for between two equal length variables for any degree or target.
#' @param degree_lpm integer; Degree for lower deviations of both variable X and Y.  \code{(degree_lpm = 0)} is frequency, \code{(degree_lpm = 1)} is area.
#' @param x a numeric vector.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param y a numeric vector of equal length to \code{x}.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param target_x numeric; Target for lower deviations of variable X.  Typically the mean of Variable X for classical statistics equivalences, but does not have to be.
#' @param target_y numeric; Target for lower deviations of variable Y.  Typically the mean of Variable Y for classical statistics equivalences, but does not have to be.
#' @return Co-LPM of two variables
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100) ; y <- rnorm(100)
#' Co.LPM(0, x, y, mean(x), mean(y))
#' @export
Co.LPM <- function(degree_lpm, x, y, target_x, target_y) {
    .Call(`_NNS_CoLPM_RCPP`, degree_lpm, x, y, target_x, target_y)
}

#' Co-Upper Partial Moment
#' (Upper Right Quadrant 1)
#'
#' This function generates a co-upper partial moment between two equal length variables for any degree or target.
#' @param degree_upm integer; Degree for upper variations of both variable X and Y.  \code{(degree_upm = 0)} is frequency, \code{(degree_upm = 1)} is area.
#' @param x a numeric vector.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param y a numeric vector of equal length to \code{x}.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param target_x numeric; Target for upside deviations of variable X.  Typically the mean of Variable X for classical statistics equivalences, but does not have to be.
#' @param target_y numeric; Target for upside deviations of variable Y.  Typically the mean of Variable Y for classical statistics equivalences, but does not have to be.
#' @return Co-UPM of two variables
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100) ; y <- rnorm(100)
#' Co.UPM(0, x, y, mean(x), mean(y))
#' @export
Co.UPM <- function(degree_upm, x, y, target_x, target_y) {
    .Call(`_NNS_CoUPM_RCPP`, degree_upm, x, y, target_x, target_y)
}

#' Divergent-Lower Partial Moment
#' (Lower Right Quadrant 3)
#'
#' This function generates a divergent lower partial moment between two equal length variables for any degree or target.
#' @param degree_lpm integer; Degree for lower deviations of variable Y.  \code{(degree_lpm = 0)} is frequency, \code{(degree_lpm = 1)} is area.
#' @param degree_upm integer; Degree for upper deviations of variable X.  \code{(degree_upm = 0)} is frequency, \code{(degree_upm = 1)} is area.
#' @param x a numeric vector.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param y a numeric vector of equal length to \code{x}.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param target_x numeric; Target for upside deviations of variable X.  Typically the mean of Variable X for classical statistics equivalences, but does not have to be.
#' @param target_y numeric; Target for lower deviations of variable Y.  Typically the mean of Variable Y for classical statistics equivalences, but does not have to be.
#' @return Divergent LPM of two variables
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100) ; y <- rnorm(100)
#' D.LPM(0, 0, x, y, mean(x), mean(y))
#' @export
D.LPM <- function(degree_lpm, degree_upm, x, y, target_x, target_y) {
    .Call(`_NNS_DLPM_RCPP`, degree_lpm, degree_upm, x, y, target_x, target_y)
}

#' Divergent-Upper Partial Moment
#' (Upper Left Quadrant 2)
#'
#' This function generates a divergent upper partial moment between two equal length variables for any degree or target.
#' @param degree_lpm integer; Degree for lower deviations of variable X.  \code{(degree_lpm = 0)} is frequency, \code{(degree_lpm = 1)} is area.
#' @param degree_upm integer; Degree for upper deviations of variable Y.  \code{(degree_upm = 0)} is frequency, \code{(degree_upm = 1)} is area.
#' @param x a numeric vector.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param y a numeric vector of equal length to \code{x}.   \link{data.frame} or \link{list} type objects are not permissible.
#' @param target_x numeric; Target for lower deviations of variable X.  Typically the mean of Variable X for classical statistics equivalences, but does not have to be.
#' @param target_y numeric; Target for upper deviations of variable Y.  Typically the mean of Variable Y for classical statistics equivalences, but does not have to be.
#' @return Divergent UPM of two variables
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @examples
#' set.seed(123)
#' x <- rnorm(100) ; y <- rnorm(100)
#' D.UPM(0, 0, x, y, mean(x), mean(y))
#' @export
D.UPM <- function(degree_lpm, degree_upm, x, y, target_x, target_y) {
    .Call(`_NNS_DUPM_RCPP`, degree_lpm, degree_upm, x, y, target_x, target_y)
}

#' Partial Moment Matrix
#'
#'
#' This function generates a co-partial moment matrix for the specified co-partial moment.
#' @param LPM_degree integer; Degree for \code{variable} below \code{target} deviations.  \code{(LPM_degree = 0)} is frequency, \code{(LPM_degree = 1)} is area.
#' @param UPM_degree integer; Degree for \code{variable} above \code{target} deviations.  \code{(UPM_degree = 0)} is frequency, \code{(UPM_degree = 1)} is area.
#' @param target numeric; Typically the mean of Variable X for classical statistics equivalences, but does not have to be. (Vectorized)  \code{(target = NULL)} (default) will set the target as the mean of every variable.
#' @param variable a numeric matrix or data.frame.
#' @param pop_adj logical; \code{FALSE} (default) Adjusts the sample co-partial moment matrices for population statistics.
#' @return 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 \code{"D.LPM" and "D.UPM"} matrices, matrix is \code{D.LPM(column,row,...)}.
#' @author Fred Viole, OVVO Financial Systems
#' @references Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments"
#' \url{https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp}
#' @references Viole, F. (2017) "Bayes' Theorem From Partial Moments"
#' \url{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, 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
#' @export
PM.matrix <- function(LPM_degree, UPM_degree, target, variable, pop_adj) {
    .Call(`_NNS_PMMatrix_RCPP`, LPM_degree, UPM_degree, target, variable, pop_adj)
}

#' Fast binning of numeric vector into equidistant bins
#' 
#' Fast binning of numeric vector into equidistant bins
#'
#' Missing values (NA, Inf, NaN) are added at the end of the vector as the last bin returned if missinglast is set to TRUE
#' 
#' @param x A matrix of regressor variables. Must have the same number of rows as the length of y.
#' @param width The width of the bins
#' @param origin The starting point for the bins. Any number smaller than origin will be disregarded
#' @param missinglast Boolean. Should the missing observations be added as a separate element at the end of the returned count vector.
#' @return An list with elements counts (the frequencies), origin (the origin), width (the width), missing (the number of missings), and last_bin_is_missing (boolean) telling whether the missinglast is true or not.
#' @examples
#' \dontrun{
#' set.seed(1)
#' x <- sample(10, 20, replace = TRUE)
#' NNS_bin(x, 15)
#' }
#' @export
NNS_bin <- function(x, width, origin = 0, missinglast = FALSE) {
    .Call(`_NNS_NNS_bin`, x, width, origin, missinglast)
}