gputools (version 0.1-1)

gpuCor: Calculate Various Correlation Coefficients With a GPU

Description

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".

Usage

gpuCor(x, y = NULL, method = c("pearson", "kendall"), anyNAs = FALSE)

Arguments

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.

Value

• 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 $x_i$, $y_j$.

See Also

cor

Examples

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)