merge - Merge estimated models
Syntax
m = merge(m1,m2,....,mN)
[m,tv] = merge(m1,m2)
Description
The models m1,m2,...,mN must all be of the
same structure, just differing in parameter values and covariance
matrices. Then m is the merged model, where the
parameter vector is a statistically weighted mean (using the covariance
matrices to determine the weights) of the parameters of mk.
When two models are merged,
[m, tv] = merge(m1,m2)
returns a test variable tv. It is
distributed
with n degrees of freedom, if the parameters of m1 and m2 have
the same means. Here n is the length of the parameter
vector. A large value of tv thus indicates that
it might be questionable to merge the models.
For idfrd models, merge is
a statistical average of two responses in the individual models, weighted
using inverse variances. You can only merge two idfrd models
with responses at the same frequencies and nonzero covariances.
Merging models is an alternative to merging data sets and estimating
a model for the merged data.
m1 = arx(z1,[2 3 4]);
m2 = arx(z2,[2 3 4]);
ma = merge(m1,m2);
and
mb = arx(merge(z1,z2),[2 3 4]);
result in models ma and mb that
are related and should be close. The difference is that merging the
data sets assumes that the signal-to-noise ratios are about the same
in the two experiments. Merging the models allows one model to be
much more uncertain, for example, due to more disturbances in that
experiment. If the conditions are about the same, we recommend that
you merge data rather than models, since this is more efficient and
typically involves better conditioned calculations.
 | merge (iddata) | | midprefs |  |
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