gaussianRankCorr {BSL} R Documentation

## Gaussian rank correlation

### Description

This function computes the Gaussian rank correlation of Boudt et al. (2012).

### Usage

gaussianRankCorr(x, vec = FALSE)


### Arguments

 x A numeric matrix representing data where the number of rows is the number of independent data points and the number of columns is the number of variables in the dataset. vec A logical argument indicating if the vector of correlations should be returned instead of a matrix.

### Value

Gaussian rank correlation matrix (default) or a vector of pair correlations.

### References

Boudt K, Cornelissen J, Croux C (2012). “The Gaussian Rank Correlation Estimator: Robustness Properties.” Statistics and Computing, 22(2), 471–483. doi: 10.1007/s11222-011-9237-0.

### See Also

cor2cov for conversion from correlation matrix to covariance matrix.

### Examples

data(ma2)
model <- newModel(fnSimVec = ma2_sim_vec, fnSum = ma2_sum, simArgs = list(TT = 10),
theta0 = ma2$start, fnLogPrior = ma2_prior) set.seed(100) # generate 1000 simualtions from the ma2 model x <- simulation(model, n = 1000, theta = c(0.6, 0.2))$x

corr1 <- cor(x) # traditional correlation matrix
corr2 <- gaussianRankCorr(x) # Gaussian rank correlation matrix
oldpar <- par()
par(mfrow = c(1, 2))
image(corr1, main = 'traditional correlation matrix')
image(corr2, main = 'Gaussian rank correlation matrix')
par(mfrow = oldpar\$mfrow)

std <- apply(x, MARGIN = 2, FUN = sd) # standard deviations
cor2cov(gaussianRankCorr(x), std) # convert to covariance matrix



[Package BSL version 3.2.5 Index]