gpuCor {gputools} | R Documentation |
The correlation coefficient will be calculated for each pair $x_i$, $y_j$ where $x_i$ is a column of $x$ and $y_j$ is a column of $y$. Currently, Pearson's and Kendall's correlation coefficient are implemented. Pearson's may be calculated for data sets containing NAs in which case, the implementation behaves as R-native cor function with use="pairwise.complete".
gpuCor(x, y = NULL, method = c("pearson", "kendall"), anyNAs = FALSE)
x |
a matrix of floating point values in which each column is a random variable. |
y |
a matrix of floating point values in which each column is a random variable. |
method |
a string. Either "pearson" or "kendall". |
anyNAs |
a logical value. Set to TRUE to do Pearson's with NAs in your data set. |
For method "pearson" and anyNAs=TRUE, a list with matrices pairs,
coefficients, and ts. The matrix entry $i$, $j$ for pairs represents
the number of pairs of entries $x_i^k$, $y_j^k$ (the $k$-th entry from
$x_i$ and $y_j$ respectively) where neither are NA. These are the
number of entries actually used to calculate the coefficients.
Entry $i$, $j$ of the coefficients matrix is the correlation coeffcient
for $x_i$, $y_j$. Entry $i$, $j$ of the ts matrix is the t-score of the
$i$, $j$ entry of the coefficient matrix.
For method "pearson" and anyNAs=FALSE, a matrix of floating point
numbers where entry $i$, $j$ is the correlation coeffcient for $x_i$, $y_j$.
For method "kendall", a matrix of floating point numbers where entry
$i$, $j$ is the correlation coeffcient for $x_i$, $y_j$.
cor
numAvars <- 5 numBvars <- 10 numSamples <- 30 A <- matrix(runif(numAvars*numSamples), numSamples, numAvars) B <- matrix(runif(numBvars*numSamples), numSamples, numBvars) gpuCor(A, B, "pearson") gpuCor(A, B, "kendall") A[3,2] <- NA gpuCor(A, B, "pearson", TRUE)