Prints pairwise correlation matrix flagging statistically significant correlations using one of a few different methods.
pwCorrMat(
formula,
data,
method = c("z", "t", "sim"),
weight = NULL,
alpha = 0.05,
...
)A right-sided formula giving the variables to be correlated separated by pluses.
A data frame where the variables in the formula can be found.
A method for calculating the significance of the of the correlation - one of "z", "t" or "sim". When correlations are calculated with weights, a bootstrap is used to generate p-values regardless of the method specified. See details for more.
A vector of weightings as long as there are rows in data.
Cutoff for identifying significant correlations.
Other arguments to be passed down to sig.cor.
An object of class pwc, which is a list with elements rSig
which is a lower-triangular correlation matrix where only significant correlations
are printed, r which is the raw-data pairwise correlation matrix and
p which gives the p-values of all of the correlations.
The significance is found through one of three ways. For correlation r,
the z-transformation is .5*log((1+r)/(1-r)), the p-value for which is found using
the standard normal distribution. The t-transformation is r*sqrt((n-2)/(1-r^2)),
the p-value for which is found using a t-distribution with n-2 degrees of freedom.
The "sim" method uses a permutation test to build the sampling distribution of the
correlation under the null hypothesis and then calculates a p-value from that
distribution.