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The run() method runs the simulation on basis of a given
specification and coefficients.
Input:
- specification
- - an instance of class EquationSpecification
specifying variables to be used in the simulation.
- coefficients
- - an instance of class Coefficients that contains
values of coefficients to be used in the simulation.
- dataset
- - a Dataset representing the whole set of observations.
- index
- - an index within
dataset determining for which
observations the prediction is to be made. If it is not given, the whole
dataset is considered.
- chunk_specification
- - a dictionary specifying how to determine the number
of chunks to run the model in. It is passed to the run method of ChunkModel
(see Section 22.4.2).
- data_objects
- - a dictionary containing additional datasets
needed for computing variables. This argument is obsolete - the datasets should be
included in the
dataset_pool argument of the constructor.
- run_config
- - additional Resources (or dictionary) for controlling the simulation
run.
- initial_values
- - an array of initial values of the results. It is of the same size as
dataset. Elements that are handled by the model (determined by index and specification)
will be overwritten by the results. By default, the array is set to zeros.
- procedure
- - a fully qualified name of the module/class in
which the regression is implemented (see Section 22.5.5).
If it is None (default), the value of
regression_procedure from the constructor is taken.
This argument overwrites the class attribute regression.
Algorithm:
The algorithm is implemented in the method run_chunk() called from the
parent class ChunkModel for each chunk. It invokes a computation of
all variables given in the specification. Then for each submodel it creates a
data matrix for values corresponding to index and invokes the
run() method of the object stored in the class attribute
regression (see Section 22.5.5).
Output:
The method returns an array of the same size as index, determining the
outcome of the regression for each observation included in index.
Next: The Estimate Method
Up: RegressionModel Class
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