pcause {generalCorr}

Compute the bootstrap probability of correct causal direction.

Description

Maximum entropy bootstrap (‘meboot’) package is used for statistical inference regarding \delta which equals GMC(X|Y)-GMC(Y|X) defined by Zheng et al (2012). The bootstrap provides an approximation to chances of correct determination of the causal direction.

Usage

pcause(x, y, n999 = 999)

Arguments

x

Vector of x data

y

Vector of y data

n999

Number of bootstrap replications (default=999)

Value

P(cause) the bootstrap proportion of correct causal determinations.

Note

'pcause' is computer intensive and generally slow. It is better to use it at a later stage in the investigation when a preliminary causal determination is already made. Its use may slow the exploratory phase. In my experience, if P(cause) is less than 0.55, there is a cause for concern.

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, doi:10.1080/03610918.2015.1122048

Zheng, S., Shi, N.-Z., and Zhang, Z. (2012). Generalized measures of correlation for asymmetry, nonlinearity, and beyond. Journal of the American Statistical Association, vol. 107, pp. 1239-1252.

Vinod, H. D. and Lopez-de-Lacalle, J. (2009). 'Maximum entropy bootstrap for time series: The meboot R package.' Journal of Statistical Software, Vol. 29(5), pp. 1-19.

Examples

## Not run: 
set.seed(34);x=sample(1:10);y=sample(2:11)
pcause(x,y,n999=29)

data('EuroCrime')
attach(EuroCrime)
pcause(crim,off,n999=29)

## End(Not run)