gputools (version 1.1)

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

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)