R: Inverse Method Variances

invvar {ruv} | R Documentation

## Inverse Method Variances

### Description

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

### Usage

    invvar(Y, ctl, XZ = NULL, eta = NULL, lambda = NULL,
           invsvd = NULL)

### 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.
`invsvd` | Can be included to speed up execution. Generally used when calling invvar many times with different values of lambda.

### Value

A list containing

`sigma2` | Estimates of the features' variances. A vector of length n.
`df` | The "effective degrees of freedom"
`invsvd` | Can be used to speed up future calls of invvar.

### 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`

[Package ruv version 0.9.6 Index]