Description Usage Arguments Details Value Author(s) References See Also Examples

Given intensities from microRNA data, fits a robust linear model at probe level and return the residual standard deviations and the array effects.

1 2 3 4 5 | ```
estVC(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)
## S3 method for class 'RGList'
estVC(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)
## S3 method for class 'EList'
estVC(object,method=c("joint","rlm"),cov.formula=c("weighted","asymptotic"),clName,verbose=FALSE)
``` |

`object` |
an object of class |

`method` |
character string specifying the estimating algorithm to be used. Choices are "joint" and "rlm". |

`cov.formula` |
character string specifying the covariance formula to be used. Choices are "weighted" and "asymptotic". |

`clName` |
Cluster object produced by |

`verbose` |
Print some debug messages. |

`estVC`

is the first step in LVS normalization. It fits a robust linear model at the probe-level data in order to estimate the variability of probe intensities due to array-to-array variability. Depending on whether probes show considerable differences in within-probe variance, user can choose the more complex `joint`

model to accommodate the potential heteroscedasticity or standard robust linear model if within-probe variance can be ignored.

The array effects are then captured by the chi-square statistic. The covariance matrix can be estimated based either on the sandwich form of weighted covariance matrix or an asymptotic form.

An object of class `RA`

containing three components as follows:

`ArrayEffects` |
a matrix containing the array effect with samples as columns and miRNAs as rows. |

`ArrayChi2` |
vector giving chi-square statisitcs of the miRNAs as a measure of array-to-array variability. |

`logStdDev` |
vector giving standard deviations of the genes on log scale. |

Stefano Calza <stefano.calza@biostatistics.it>, Suo Chen and Yudi Pawitan.

Calza et al., 'Normalization of oligonucleotide arrays based on the least variant set of genes', (2008, BMCBioinformatics); Pawitan, Y. 'In All Likelihood: Statistical Modeling and Inference Using Likelihood', (2001, Oxford University Press); Huber, P. J., 'Robust estimation of a location parameter', (1964, Annuas of Mathematical Statistics).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## Not run:
# Starting from an EList object called MIR
data("MIR-spike-in")
AA <- estVC(MIR,method="joint")
# Parellel execution using multicore
library(multicore)
# use this to set the desided number of
#cores. Otherwise multicore would use all the available
options(cores=8)
AA <- estVC(MIR,method="joint")
detach('package:multicore')
# Parellel execution using snow
library(snow)
cl <- makeCluster(8,type="SOCK")
# Or also...see ?makeCluster
# cl <- makeCluster(8,type="MPI")
AA <- estVC(MIR,method="joint",clName=cl)
## End(Not run)
``` |

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