cor0.test
(Package: GeneNet) :
Test of Vanishing (Partial) Correlation
cor0.test computes a p-value for the two-sided test with the null hypothesis H0: rho == 0 versus the alternative hypothesis HA: rho != 0.
● Data Source:
CranContrib
● Keywords: htest
● Alias: cor0.test
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GeneNet is a package for analyzing gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Sch"afer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: GeneNet-package
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ggm.estimate.pcor
(Package: GeneNet) :
Graphical Gaussian Models: Small Sample Estimation of Partial Correlation
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.
● Data Source:
CranContrib
● Keywords: htest
● Alias: ggm.estimate.pcor
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kappa2n
(Package: GeneNet) :
Relationship Between Sample Size and the Degree of Freedom of Correlation Distribution
The function kappa2n returns the sample size that corresponds to a given degree of freedom kappa, whereas n2kappa converts sample size to the corresponding degree of freedom.
● Data Source:
CranContrib
● Keywords: univar
● Alias: kappa2n, n2kappa
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ggm.simulate.data
(Package: GeneNet) :
Graphical Gaussian Models: Simulation of Data
ggm.simulate.data takes a positive definite partial correlation matrix and generates an i.i.d. sample from the corresponding standard multinormal distribution (with mean 0 and variance 1). If the input matrix pcor is not positive definite an error is thrown.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: ggm.simulate.data
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z.transform
(Package: GeneNet) :
Variance-Stabilizing Transformations of the Correlation Coefficient
z.transform implements Fisher's (1921) first-order and Hotelling's (1953) second-order transformations to stabilize the distribution of the correlation coefficient. After the transformation the data follows approximately a normal distribution with constant variance (i.e. independent of the mean).
● Data Source:
CranContrib
● Keywords: univar
● Alias: hotelling.transform, z.transform
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network.test.edges
(Package: GeneNet) :
Graphical Gaussian Models: Assess Significance of Edges (and Directions)
network.test.edges returns a data frame containing all edges listed in order of the magnitude of the partial correlation associated with each edge. If fdr=TRUE then in addition the p-values, q-values and posterior probabilities (=1 - local fdr) for each potential edge are computed.
● Data Source:
CranContrib
● Keywords: htest
● Alias: extract.network, network.test.edges
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network.make.graph
(Package: GeneNet) :
Graphical Gaussian Models: Plotting the Network
network.make.dot converts an edge list as obtained by network.test.edges into a "dot" file that can directly be used for plotting the network with graphviz.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: edge.info, network.make.dot, network.make.graph, node.degree, num.nodes
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ggm.simulate.pcor
(Package: GeneNet) :
Graphical Gaussian Models: Simulation of Networks
ggm.simulate.pcor generates a random matrix of partial correlations that corresponds to a GGM network of a given size (num.nodes) with a specified fraction of non-zero edges.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: ggm.simulate.pcor
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Internal GeneNet functions.
● Data Source:
CranContrib
● Keywords: internal
● Alias: ggm.list.edges, myrmvnorm
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