estimate.hyperparameters: estimate.hyperparameters
In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

Description Usage Arguments Value Author(s) References Examples

Hyperparameter estimation.

estimate.hyperparameters(sets = NULL, probe.parameters = list(alpha = 2,
  beta = 1), batches, cdf = NULL, bg.method = "rma", epsilon = 0.01,
  load.batches = FALSE, save.hyperparameter.batches = FALSE, mc.cores = 1,
  verbose = TRUE, normalization.method = "quantiles",
  save.batches.dir = ".", unique.run.identifier = NULL,
  set.inds = set.inds)

`sets`	Probesets to handle. All probesets by default.
`probe.parameters`	User-defined priors. May also include quantile.basis
`batches`	Data batches for online learning
`cdf`	CDF probeset definition file
`bg.method`	Background correction method
`epsilon`	Convergence parameter
`load.batches`	Logical. Load preprocessed data whose identifiers are picked from names(batches). Assuming that the same batch list (batches) was used to create the files in online.quantiles function.
`save.hyperparameter.batches`	Save hyperparameters for each batch into files using the identifiers with batch name with -hyper.RData suffix.
`mc.cores`	Number of cores for parallel computation
`verbose`	Print progress information
`normalization.method`	Normalization method
`save.batches.dir`	Specify the output directory for temporary batch saves.
`unique.run.identifier`	Define identifier for this run for naming the temporary batch files. By default, a random id is generated.
`set.inds`	Probeset indices

alpha: Hyperparameter alpha (same for all probesets); betas: Hyperparameter beta (probe-specific); variances: Probe-specific variances (beta/alpha)

Leo Lahti leo.lahti@iki.fi

See citation("RPA")

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RPA Copyright (C) 2008-2016 Leo Lahti.
This program comes with ABSOLUTELY NO WARRANTY.
This is free software, and you are welcome to redistribute it under the FreeBSD open source license.

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