R: (Randomization) Inverse Method Variances

randinvvar {ruv} | R Documentation

## (Randomization) Inverse Method Variances

### Description

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.

### Usage

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

### Arguments

`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 oposed 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.

### Value

A list containing

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

### Author(s)

Johann Gagnon-Bartsch johanngb@umich.edu

### References

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.

### See Also

`RUVinv`, `RUVrinv`, `invvar`

[Package ruv version 0.9.6 Index]