dyn.cor {longitudinal} | R Documentation |
The functions estimate the dynamical correlation and covariance, and related quantities.
dyn.cor(x, lambda, verbose=TRUE) dyn.cov(x, lambda, lambda.var, verbose=TRUE) dyn.invcor(x, lambda, verbose=TRUE) dyn.invcov(x, lambda, lambda.var, verbose=TRUE) dyn.pcor(x, lambda, verbose=TRUE) dyn.pcov(x, lambda, lambda.var, verbose=TRUE)
x |
a data matrix |
lambda |
the correlation shrinkage intensity (range 0-1). If lambda is not specified
(the default) it is estimated using an analytic formula from Schaefer
and Strimmer (2005)and Opgen-Rhein and Strimmer (2006).
For lambda=0 the empirical correlations are recovered.
See also cor.shrink . |
lambda.var |
the variance shrinkage intensity (range 0-1). If lambda.var is not specified
(the default) it is estimated using an analytic formula from Schaefer
and Strimmer (2005) and Opgen-Rhein and Strimmer (2006).
For lambda.var=0 the empirical variances are recovered.
See also var.shrink . |
verbose |
report progress while computing (default: TRUE) |
The dynamical correlation and related quantities implemented here follow the definition of Opgen-Rhein and K. Strimmer (2006a,b). This approach derives from a FDA perspective. Essentially, it takes account of the distances between the various time points by introducing weights. If these weights are all equal the usual estimators are obtained.
For details about the analytic shrinkage procedure consult
Opgen-Rhein and K. Strimmer (2006b) and Schaefer and Strimmer (2005)
as well as the help page of cov.shrink
.
dyn.cor
returns the dynamical correlation matrix.
dyn.cov
returns the dynamical covariance matrix.
dyn.invcor
returns the inverse dynamical correlation matrix.
dyn.invcov
returns the inverse dynamical covariance matrix.
dyn.pcor
returns the partial dynamical correlation matrix.
dyn.pcov
returns the partial dynamical covariance matrix.
Rainer Opgen-Rhein (http://www.statistik.lmu.de/~opgen/) and Korbinian Strimmer (http://www.statistik.lmu.de/~strimmer/).
Opgen-Rhein, R., and K. Strimmer. 2006a. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4:53-65. (http://http://www.ine.pt/revstat/)
Opgen-Rhein, R., and K. Strimmer. 2006b. Using regularized dynamic correlation to infer gene dependency networks from time-series microarray data. The 4th International Workshop on Computational Systems Biology, WCSB 2006 (June 12-13, 2006, Tampere, Finland). (http://www.cs.tut.fi/wcsb06/)
Schaefer, J., and Strimmer, K. (2005). A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4:32. (http://www.bepress.com/sagmb/vol4/iss1/art32/)
{dyn.weights}
, cov.shrink
# load "longitudinal" library library("longitudinal") # load tcell data data(tcell) get.time.repeats(tcell.34) # dynamical partial correlation # (this takes into account the unequally distant time points) dynpc <- dyn.pcor(tcell.34, lambda=0) # static partial correlation statpc <- pcor.shrink(tcell.34, lambda=0) # this is NOT the same sum((dynpc - statpc)^2)