Estimates Pearson correlation coefficients (PCCs) and their corresponding P-values between all pairs of variables from an input data set.
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data set from where to estimate the Pearson correlation coefficients. It can be an ExpressionSet object, a data frame or a matrix.
logical; if TRUE it is assumed
The calculations made by this function are the same as the ones made for
a single pair of variables by the function
cor.test but for
all the pairs of variables in the data set, with the exception of the treatment
of missing values, since only complete observations across all variables in
X are used.
A list with two matrices, one with the estimates of the PCCs and the other with their P-values.
R. Castelo and A. Roverato
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require(graph) require(mvtnorm) nVar <- 50 ## number of variables nObs <- 10 ## number of observations to simulate set.seed(123) g <- randomEGraph(as.character(1:nVar), p=0.15) Sigma <- qpG2Sigma(g, rho=0.5) X <- rmvnorm(nObs, sigma=as.matrix(Sigma)) pcc.estimates <- qpPCC(X) ## get the corresponding boolean adjacency matrix A <- as(g, "matrix") == 1 ## Pearson correlation coefficients of the present edges summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & A])) ## Pearson correlation coefficients of the missing edges summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & !A]))
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