gpuGranger {gputools} | R Documentation |
This function performs, with the aid of a GPU, Granger Causality Tests on permutations of pairs of columns of the input matrixes 'x' and 'y'.
gpuGranger(x, y=NULL, lag)
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 fTests and pValues. The fTests matrix
holds the F-statistics from the Granger causality tests. The pValues matrix
holds rough estimations of the p-value for the F-statistic of the same
row. Note that the p-values for F-statistics greater than magnitude one
differ from that calculated by the package MSBVAR's 'granger.test'.
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 fTests[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 fTests[i, j]
and pValues[i, j].
# 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(randVarSequences, lag = 5) print(result)