EstHyper: Estimate the Hyper-parameter Using Generalized Least Squares
In StructFDR: False Discovery Control Procedure Integrating the Prior Structure Information
Description
Usage
Arguments
Value
Author(s)
References
Description
Estimate the hyper-parameter by calling the gls function. The errors are allowed to be correlated and/or have unequal variances.
Usage
1
Arguments
y
a vector of z-values.
D
a distance matrix defined based on the prior structure. Diagonal have to be zeros.
init.val
initial values for the transformed hyper-parameter. Default is 0.
Value
a vector of estimated hyper-parameter values plus log likelihood.
Author(s)
Jun Chen
References
Jian Xiao, Hongyuan Cao and Jun Chen (2016). False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Submitted.
StructFDR documentation built on May 2, 2019, 9:44 a.m.
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Description Usage Arguments Value Author(s) References
Description
Estimate the hyper-parameter by calling the gls function. The errors are allowed to be correlated and/or have unequal variances.
Usage
1 |
Arguments
y |
a vector of z-values. |
D |
a distance matrix defined based on the prior structure. Diagonal have to be zeros. |
init.val |
initial values for the transformed hyper-parameter. Default is 0. |
Value
a vector of estimated hyper-parameter values plus log likelihood.
Author(s)
Jun Chen
References
Jian Xiao, Hongyuan Cao and Jun Chen (2016). False discovery rate control incorporating phylogenetic tree increases detection power in microbiome-wide multiple testing. Submitted.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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