qpPCC {qpgraph} | R Documentation |
Estimates Pearson correlation coefficients (PCCs) and their corresponding P-values between all pairs of variables from an input data set.
## S4 method for signature 'ExpressionSet': qpPCC(data, long.dim.are.variables=TRUE) ## S4 method for signature 'data.frame': qpPCC(data, long.dim.are.variables=TRUE) ## S4 method for signature 'matrix': qpPCC(data, long.dim.are.variables=TRUE)
data |
data set from where to estimate the Pearson correlation coefficients. It can be an ExpressionSet object, a data frame or a matrix. |
long.dim.are.variables |
logical; if TRUE it is assumed that when data are in a data frame or in a matrix, the longer dimension is the one defining the random variables (default); if FALSE, then random variables are assumed to be at the columns of the data frame or matrix. |
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.
A list with two matrices, one with the estimates of the PCCs and the other with their P-values.
R. Castelo and A. Roverato
nVar <- 50 # number of variables maxCon <- 5 # maximum connectivity per variable nObs <- 30 # number of observations to simulate I <- qpRndGraph(n.vtx=nVar, n.bd=maxCon) K <- qpI2K(I) X <- qpSampleMvnorm(K, nObs) pcc.estimates <- qpPCC(X) # Pearson correlation coefficients of the present edges summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & I])) # Pearson correlation coefficients of the missing edges summary(abs(pcc.estimates$R[upper.tri(pcc.estimates$R) & !I]))