fit.ProbeLevelModel: Estimates the model parameters

fit.ProbeLevelModelR Documentation

Estimates the model parameters

Description

Estimates the model parameters for all or a subset of the units.

Usage

## S3 method for class 'ProbeLevelModel'
fit(this, units="remaining", ..., force=FALSE, ram=NULL, verbose=FALSE)

Arguments

units

The units to be fitted. If NULL, all units are considered. If remaining, only non-fitted units are considered.

...

Arguments passed to readUnits().

force

If TRUE, already fitted units are re-fitted, and cached data is re-read.

ram

A double indicating if more or less units should be loaded into memory at the same time.

verbose

See Verbose.

Details

All estimates are stored to file.

The non-array specific parameter estimates together with standard deviation estimates and convergence information are stored in one file.

The parameter estimates specific to each array, typically "chip effects", are stored in array specific files.

Data set specific estimates [L = number of probes]: phi [L doubles] (probe affinities), sd(phi) [L doubles], isOutlier(phi) [L logicals]

Algorithm-specific results: iter [1 integer], convergence1 [1 logical], convergence2 [1 logical] dTheta [1 double] sd(eps) - [1 double] estimated standard deviation of the error term

Array-specific estimates [K = nbr of arrays]: theta [K doubles] (chip effects), sd(theta) [K doubles], isOutlier(theta) [K logicals]

For each array and each unit group, we store: 1 theta, 1 sd(theta), 1 isOutlier(theta), i.e. (float, float, bit) => For each array and each unit (with G_j groups), we store: G_j theta, G_j sd(theta), G_j isOutlier(theta), i.e. G_j*(float, float, bit). For optimal access we store all thetas first, then all sd(theta) and the all isOutlier(theta). To keep track of the number of groups in each unit, we have to have a (unit, ngroups) map. This can be obtained from getUnitNames() for the AffymetrixCdfFile class.

Value

Returns an integer vector of indices of the units fitted, or NULL if no units was (had to be) fitted.

Author(s)

Henrik Bengtsson

See Also

For more information see ProbeLevelModel.


aroma.affymetrix documentation built on July 18, 2022, 5:07 p.m.