gputools (version 0.26)

gpuGranger: Perform Granger Causality Tests for Vectors on a GPU

Description

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)

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.

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.

  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[ ,j] Granger-cause variable x[ ,i]?", look at fStatistics[i, j] and pValues[i, j].

  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 y[ ,j] Granger-cause variable x[ ,i]?", look at fStatistics[i, j] and pValues[i, j].

Examples

# 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)