# NNS.caus: NNS Causation In NNS: Nonlinear Nonparametric Statistics

## Description

Returns the causality from observational data between two variables.

## Usage

 `1` ```NNS.caus(x, y = NULL, factor.2.dummy = FALSE, tau = 0, plot = FALSE) ```

## Arguments

 `x` a numeric vector, matrix or data frame. `y` `NULL` (default) or a numeric vector with compatible dimensions to `x`. `factor.2.dummy` logical; `FALSE` (default) Automatically augments variable matrix with numerical dummy variables based on the levels of factors. Includes dependent variable `y`. `tau` options: ("cs", "ts", integer); 0 (default) Number of lagged observations to consider (for time series data). Otherwise, set `(tau = "cs")` for cross-sectional data. `(tau = "ts")` automatically selects the lag of the time series data, while `(tau = [integer])` specifies a time series lag. `plot` logical; `FALSE` (default) Plots the raw variables, tau normalized, and cross-normalized variables.

## Value

Returns the directional causation (x —> y) or (y —> x) and net quantity of association. For causal matrix, directional causation is returned as ([column variable] —> [row variable]). Negative numbers represent causal direction attributed to [row variable].

## 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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## Not run: ## x causes y... set.seed(123) x <- rnorm(1000) ; y <- x ^ 2 NNS.caus(x, y, tau = "cs") ## Causal matrix without per factor causation NNS.caus(iris, tau = 0) ## Causal matrix with per factor causation NNS.caus(iris, factor.2.dummy = TRUE, tau = 0) ## End(Not run) ```

NNS documentation built on June 26, 2021, 1:07 a.m.