summarizeFarmsExact2 {cn.farms} | R Documentation

## Summarization Laplacian approach with exact computation

### Description

This function implements an exact Laplace FARMS algorithm.

### Usage

    summarizeFarmsExact2(probes, mu = 1, weight = 0.5, weightSignal = 1,
      weightZ = 1, weightProbes = TRUE, cyc = c(10, 10), tol = 1e-05,
      weightType = "mean", centering = "median", rescale = FALSE,
      backscaleComputation = FALSE, maxIntensity = TRUE, refIdx, ...)

### Arguments

`probes` | A matrix with numeric values.
`mu` | Hyperparameter value which allows to quantify different aspects of potential prior knowledge. Values near zero assumes that most positions do not contain a signal, and introduces a bias for loading matrix elements near zero. Default value is 0 and it's recommended not to change it.
`weight` | Hyperparameter value which determines the influence of the Gaussian prior of the loadings
`weightSignal` | Hyperparameter value on the signal.
`weightZ` | Hyperparameter value which determines how strong the Laplace prior of the factor should be at 0. Users should be aware, that a change of weightZ in comparison to the default parameter might also entail a need to change other parameters. Unexperienced users should not change weightZ.
`weightProbes` | Parameter (TRUE/FALSE), that determines, if the number of probes should additionally be considered in weight. If TRUE, weight will be modified.
`cyc` | Number of cycles. If the length is two, it is assumed, that a minimum and a maximum number of cycles is given. If the length is one, the value is interpreted as the exact number of cycles to be executed (minimum == maximum).
`tol` | States the termination tolerance if cyc[1]!=cyc[2]. Default is 0.00001.
`weightType` | Flag, that is used to summarize the probes of a sample.
`centering` | States how the data should be centered ("mean", "median"). Default is median.
`rescale` | Parameter (TRUE/FALSE), that determines, if moments in exact Laplace FARMS are rescaled in each iteration. Default is FALSE.
`backscaleComputation` | Parameter (TRUE/FALSE), that determines if the moments of hidden variables should be reestimated after rescaling the parameters.
`maxIntensity` | Parameter (TRUE/FALSE), that determines if the expectation value (=FALSE) or the maximum value (=TRUE) of p(z|x_i) should be used for an estimation of the hidden varaible.
`refIdx` | index or indices which are used for computation of the centering
`...` | Further parameters for expert users.

### Value

A list including: the found parameters: lambda0, lambda1, Psi

the estimated factors: z (expectation), maxZ (maximum)

p: log-likelihood of the data given the found lambda0, lambda1, Psi (not the posterior likelihood that is optimized)

varzx: variances of the hidden variables given the data

KL: Kullback Leibler divergences between between posterior and prior distribution of the hidden variables

IC: Information Content considering the hidden variables and data

ICtransform: transformed Information Content

Case: Case for computation of a sample point (non-exception, special exception)

L1median: Median of the lambda vector components

intensity: back-computed summarized probeset values with mean correction

L_z: back-computed summarized probeset values without mean correction

rawCN: transformed values of L_z

SNR: some additional signal to noise ratio value

### Author(s)

Andreas Mayr mayr@bioinf.jku.at and Djork-Arne Clevert okko@clevert.de and Andreas Mitterecker mitterecker@bioinf.jku.at

### Examples

    x <- matrix(rnorm(100, 11), 20, 5)
    summarizeFarmsExact(x)

[Package cn.farms version 1.32.0 Index]