Description Usage Arguments Value Examples
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'.
1 | gpuGranger(x, y=NULL, lag)
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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. |
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].
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | # 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)
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