qpPCC {qpgraph}R Documentation

Estimation of Pearson correlation coefficients

Description

Estimates Pearson correlation coefficients (PCCs) and their corresponding P-values between all pairs of variables from an input data set.

Usage

## 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)

Arguments

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.

Details

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.

Value

A list with two matrices, one with the estimates of the PCCs and the other with their P-values.

Author(s)

R. Castelo and A. Roverato

See Also

qpPAC

Examples

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]))


[Package qpgraph version 1.0.0 Index]