# 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, tau, time.series = FALSE, plot = FALSE) ```

## Arguments

 `x` a numeric vector, matrix or data frame. `y` `NULL` (default) or a numeric vector with compatible dimsensions to `x`. `tau` options: ("cs", "ts", integer); 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. `time.series` logical; `FALSE` (default) If analyzing time series data with `tau = [integer]`, select `(time.series = TRUE)`. Not required when `(tau = "ts")`. `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" http://amzn.com/1490523995

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## x clearly causes y... set.seed(123) x <- rnorm(100) ; y <- x ^ 2 NNS.caus(x, y, tau = "cs") x <- 1:100 ; y <- x^2 NNS.caus(x, y, tau = "ts", time.series = TRUE) ## Causal matrix ## Not run: NNS.caus(data.matrix(iris), tau = 0) ## End(Not run) ```

NNS documentation built on April 15, 2019, 5:05 p.m.