Description Usage Arguments Value Author(s) Examples

View source: R/summarizeFarmsLaplaceExact3.R

This function implements an exact Laplace FARMS algorithm.

1 2 3 4 5 |

`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. |

`updateSignal` |
updateSignal. |

`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. |

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

Andreas Mayr [email protected] and Djork-Arne Clevert [email protected] and Andreas Mitterecker [email protected]

1 2 | ```
x <- matrix(rnorm(100, 11), 20, 5)
summarizeFarmsExact(x)
``` |

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