NNS Causation — NNS.caus • NNS

Returns the causality from observational data between two variables.

Usage

NNS.caus(
  x,
  y = NULL,
  factor.2.dummy = FALSE,
  tau = 0,
  plot = FALSE,
  p.value = FALSE,
  nperm = 100L,
  permute = c("y", "x", "both"),
  seed = NULL,
  conf.int = 0.95
)

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.
p.value: logical; FALSE (default) If TRUE, runs a permutation test to compute empirical p-values for the signed causation from x -> y.
nperm: integer; number of permutations to use when p.value = TRUE. Default 100.
permute: one of "both", "y", or "x"; which variable(s) to shuffle when constructing the null distribution.
seed: optional integer seed for reproducibility of the permutation test.
conf.int: numeric; 0.95 (default) confidence level for the partial-moment based interval computed on the permutation null distribution.

Value

If p.value=FALSE returns the original causation vector of length 3 (directional given/received and net), named either "C(x—>y)" or "C(y—>x)" in the third slot. If p.value=TRUE returns a list with components: * causation: the original causation vector as above. * p.value: a list with empirical two-sided and one-sided p-values (x_causes_y, y_causes_x), the null distribution, the observed signed statistic, and metadata (permute, nperm). If p.value=TRUE for a matrix, the function returns a list with components: * causality: the causality matrix. * lower_CI: matrix of lower confidence bounds (partial-moment based). * upper_CI: matrix of upper confidence bounds (partial-moment based). * p.value: matrix of empirical two-sided p-values.

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)

Author

Fred Viole, OVVO Financial Systems

Examples


if (FALSE) { # \dontrun{
## 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)
} # }