ggm.estimate.pcor offers an interface to two related shrinkage estimators
of partial correlation. Both are fast, statistically efficient, and can be used for
analyzing small sample data.
The default method "statics" employs the function
pcor.shrink whereas the "dynamic" method relies on
dyn.pcor (in the
longitudinal package). The difference between the two estimators
is that the latter takes the spacings between time points into account
if the input are multiple time course data (these must be provided as
data matrix (each rows corresponds to one multivariate observation)
method used to estimate the partial correlation matrix. Available options are "static" (the default) and "dynamic" - both are shrinkage methods.
options passed to
For details of the shrinkage estimators we refer to Opgen-Rhein and Strimmer (2006a,b)
and Sch\"afer and Strimmer (2005), as well as to the manual pages of
pcor.shrink (in the
corpcor package) and
dyn.pcor (in the
Previously, this function offered several furthers options. The old option called "shrinkage" corresponds to the present "static" option. The other old options "observed.pcor", "partial.bagged.cor", and "bagged.pcor" are now considered obselete and have been removed.
An estimated partial correlation matrix.
Rainer Opgen-Rhein, Juliane Sch\"afer, and Korbinian Strimmer (http://strimmerlab.org).
Opgen-Rhein, R., and K. Strimmer. 2006a. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4:53-65. (https://www.ine.pt/revstat/tables.html)
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).
Sch\"afer, 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/)
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## Not run: # load GeneNet library library("GeneNet") # generate random network with 40 nodes # it contains 780=40*39/2 edges of which 5 percent (=39) are non-zero true.pcor <- ggm.simulate.pcor(40) # simulate data set with 40 observations m.sim <- ggm.simulate.data(40, true.pcor) # simple estimate of partial correlations estimated.pcor <- cor2pcor( cor(m.sim) ) # comparison of estimated and true values sum((true.pcor-estimated.pcor)^2) # a slightly better estimate ... estimated.pcor.2 <- ggm.estimate.pcor(m.sim) sum((true.pcor-estimated.pcor.2)^2) ## End(Not run)
Loading required package: corpcor Loading required package: longitudinal Loading required package: fdrtool  815.7756 Estimating optimal shrinkage intensity lambda (correlation matrix): 0.4113  10.07027
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