arrayWeights {limma} | R Documentation |

Estimates relative quality weights for each array in a multi-array experiment with replication.

arrayWeights(object, design = NULL, weights = NULL, method = "genebygene", maxiter = 50, tol = 1e-10, trace=FALSE) arrayWeightsSimple(object, design = NULL, maxiter = 100, tol = 1e-10, trace=FALSE)

`object` |
object of class `numeric` , `matrix` , `MAList` , `marrayNorm` ,
`exprSet` or `PLMset` containing log-ratios or log-values of
expression for a series of microarrays. |

`design` |
the design matrix of the microarray experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates. |

`weights` |
optional numeric matrix containing prior weights for each spot. |

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

`maxiter` |
maximum number of iterations allowed. |

`tol` |
convergence tolerance. |

`trace` |
logical variable. If true then output diagnostic information at each iteration of '"reml"' algorithm. |

The relative reliability of each array is estimated by measuring how well the expression values for that array follow the linear model.

A heteroscedastic model is fitted to the expression values for
each gene by calling the function `lm.wfit`

. The dispersion model
is fitted to the squared residuals from the mean fit, and is set up to
have array specific coefficients, which are updated in either full REML
scoring iterations, or using an efficient gene-by-gene update algorithm.
The final estimates of these array variances are converted to weights.

The arguments `design`

and `weights`

will be extracted from the data
`object`

if available and do not normally need to be set explicitly in
the call; if any of these are set in the call then they will over-ride
the slots or components in the data `object`

.

If `object`

is a `PLMset`

, then expression values will be taken
from the slot `chip.coefs`

and weights will be computed from
`se.chip.coefs`

. If `object`

is an `exprSet`

, then expression
values will be taken from the `exprs`

slot, but weights will not
be computed.

`arrayWeightsSimple`

is a fast version of `arrayWeights`

with `method="reml"`

, no prior weights and no missing values.

A vector of array weights.

Matthew Ritchie

An overview of linear model functions in limma is given by 06.LinearModels.

## Not run: array.wts <- arrayWeights(MA, design) w <- MA$weights if(is.null(w)) w <- array(1,dim(MA)) w <- matvec(w,array.wts) fit.wts <- lmFit(MA, design, weights=w) ## End(Not run)

[Package *limma* version 2.4.7 Index]