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.
randinvvar(Y, ctl, XZ = NULL, eta = NULL, lambda = NULL,
iterN = 1e+05)The data. A m by n matrix, where m is the number of samples and n is the number of features.
The negative controls. A logical vector of length n.
A m by (p + q) matrix containing both the factor(s) of interest (X) and known covariates (Z).
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.
Ridge parameter. If specified, the ridged inverse method will be used.
The number of random "factors of interest" to generate.
A list containing
Estimates of the features' variances. A vector of length n.
The "effective degrees of freedom"
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.