fit.ProbeLevelModel | R Documentation |
Estimates the model parameters for all or a subset of the units.
## S3 method for class 'ProbeLevelModel'
fit(this, units="remaining", ..., force=FALSE, ram=NULL, verbose=FALSE)
units |
The units to be fitted.
If |
... |
Arguments passed to |
force |
If |
ram |
A |
verbose |
See |
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.
Returns an integer
vector
of indices of the units fitted,
or NULL
if no units was (had to be) fitted.
Henrik Bengtsson
For more information see ProbeLevelModel
.
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