NNS (version 0.5.6)

NNS.caus: NNS Causation

Description

Returns the causality from observational data between two variables.

Usage

NNS.caus(x, y, 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 dimsensions 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].

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

# NOT RUN {
# }
# 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)
# }