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Perform Granger Causality Tests for Vectors on a GPU

gpuGranger.Rd

This function performs, with the aid of a GPU, Granger Causality Tests on permutations of pairs of columns of the input matrices 'x' and 'y'.

Usage

gpuGranger(x, y=NULL, lag,
    precision = getOption("gputools.precision", "single"))

Arguments

x

a matrix of floating point values. Each column represents a sequence of observations for a single random variable.

y

an optional matrix of floating point values. Each column represents a sequence of observations for a single random variable.

lag

a positive integer by which to offset the sequence of observations to calculate the coefficient for Granger causality.

precision

"single" or "double": the floating point precision the GPU computes in. Single precision is the default, set with options(gputools.precision = "double"); it runs many times faster on most consumer GPUs. Results are returned as doubles either way.

Value

a list of two single precision floating point matrices both of the same dimension. The two matrices are fStatistics and pValues. The fStatistics matrix holds the F-statistics from the Granger causality tests. Each element of the pValues matrix is the p-value for the corresponding element of the fStatistics matrix.

Both matrices have dimnames cause (rows) and effect (columns), taken from the column names of x and y.

If y is NULL, the test is run on permutations of pairs of columns of x. To find the Granger causality F-statistic estimating the answer to "Does variable x[ ,i] Granger-cause variable x[ ,j]?", look at fStatistics[i, j] and pValues[i, j]. The diagonal, a variable against itself, is NA.

If y is not NULL, the test is run on permutations of pairs (x[ ,i], y[ ,j]). To find the Granger causality F-statistic estimating the answer to "Does variable x[ ,i] Granger-cause variable y[ ,j]?", look at fStatistics[i, j] and pValues[i, j].

Examples

if (FALSE) { # gpuAvailable()
# permutations of pairs of cols of just x
numRandVars <- 5
numSamples <- 20
randVarSequences <- matrix(runif(numRandVars*numSamples), numSamples,
  numRandVars)
gpuGranger(randVarSequences, lag = 5)

# pairs of cols, one from x and one from y
numXRandVars <- 5
numXSamples <- 20
x <- matrix(runif(numXRandVars*numXSamples), numXSamples, numXRandVars)

numYRandVars <- 3
numYSamples <- 20
y <- matrix(runif(numYRandVars*numYSamples), numYSamples, numYRandVars)

result <- gpuGranger(x, y, lag = 5)
print(result)
}