User`s guide

3 Linear Model Identification

Merging Model Objects

You can merge models of the same structure to obtain a s ingle model with

parameters that are statistically weighed means of the parameters of the

individual models. When computing the merged model, the covariance

matrices of the individual models determine the w eights of the parameters.

You can perform the merge operation for the

idarx, idgrey, idpoly, idproc,

and

idss model objects.

Note Each merge operation merges thesametypeofmodelobject.

Merging models is an alte rnative to merging data sets into a sing le

multiexperiment data set, and then estimating a model for the merged data.

Whereas merging data sets assumes that the signal-to-noise ratios are about

the s ame in the two experiments, merging models allows greater v ariations

in model uncertainty, which might result from greater disturbances in an

experiment.

When the experimental conditions are about the same, merge the data

instead of models. This approach is more ef ﬁcient and typically involves

better-conditioned calculations. Fo r more information about merging data

sets into a multiexperiment data set, see “Creating Multiexperiment Data at

the Comm and Line” on page 1-53.

For more information about merging models, s ee the

merge reference page.

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