Calculation of biweight midcorrelations and associated p-values
A faster, one-step calculation of Student correlation p-values for multiple biweight midcorrelations, properly taking into account the actual number of observations.
bicorAndPvalue(x, y = NULL,
use = "pairwise.complete.obs",
alternative = c("two.sided", "less", "greater"),
...)x |
a vector or a matrix |
y |
a vector or a matrix. If |
use |
determines handling of missing data. See |
alternative |
specifies the alternative hypothesis and must be (a unique abbreviation of) one of
|
... |
other arguments to the function |
The function calculates the biweight midcorrelations of a matrix or of two matrices
and the corresponding Student p-values.
The output is not as full-featured as cor.test, but can work with matrices as input.
A list with the following components, each a marix:
bicor |
the calculated correlations |
p |
the Student p-values corresponding to the calculated correlations |
Z |
Fisher transform of the calculated correlations |
t |
Student t statistics of the calculated correlations |
nObs |
Numbers of observations for the correlation, p-values etc. |
Peter Langfelder and Steve Horvath
Peter Langfelder, Steve Horvath (2012) Fast R Functions for Robust Correlations and Hierarchical Clustering. Journal of Statistical Software, 46(11), 1-17. https://www.jstatsoft.org/v46/i11/
bicor for calculation of correlations only;
cor.test for another function for significance test of correlations
# generate random data with non-zero correlation set.seed(1); a = rnorm(100); b = rnorm(100) + a; x = cbind(a, b); # Call the function and display all results bicorAndPvalue(x) # Set some components to NA x[c(1:4), 1] = NA corAndPvalue(x) # Note that changed number of observations.
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