randinvvar: (Randomization) Inverse Method Variances
In ruv: Detect and Remove Unwanted Variation using Negative Controls

Description Usage Arguments Value Author(s) References See Also

Estimate the features' variances using a stochastic version of the inverse method. This function is usually called from RUVinv and not normally intended for stand-alone use.

1 2	randinvvar(Y, ctl, XZ = NULL, eta = NULL, lambda = NULL, iterN = 1e+05)

`Y`	The data. A m by n matrix, where m is the number of samples and n is the number of features.
`ctl`	The negative controls. A logical vector of length n.
`XZ`	A m by (p + q) matrix containing both the factor(s) of interest (X) and known covariates (Z).
`eta`	Gene-wise (as opposed to sample-wise) covariates. These covariates are adjusted for by RUV-1 before any further analysis proceeds. A matrix with n columns.
`lambda`	Ridge parameter. If specified, the ridged inverse method will be used.
`iterN`	The number of random "factors of interest" to generate.

A list containing

`sigma2`	Estimates of the features' variances. A vector of length n.
`df`	The "effective degrees of freedom"

Johann Gagnon-Bartsch johanngb@umich.edu

Removing Unwanted Variation from High Dimensional Data with Negative Controls. Gagnon-Bartsch, Jacob, and Speed, 2013. Available at: http://statistics.berkeley.edu/tech-reports/820.

RUVinv, RUVrinv, invvar

ruv documentation built on Aug. 31, 2019, 1:04 a.m.