Reconstruct missing input and output data
Datae = misdata(Data)
Datae = misdata(Data,Model)
Datae = misdata(Data,Maxiter,Tol)
Datae = misdata(Data) reconstructs missing input and output data. Data is time-domain input-output data in the iddata object format. Missing data samples (both in inputs and in outputs) are entered as NaNs. Datae is an iddata object where the missing data has been replaced by reasonable estimates.
Datae = misdata(Data,Model) specifies a model used for the reconstruction of missing data. Model is any linear identified model (idtf, idproc, idgrey, idpoly, idss). If no suitable model is known, it is estimated in an iterative fashion using default order state-space models.
Datae = misdata(Data,Maxiter,Tol) specifies maximum number of iterations and tolerance. Maxiter is the maximum number of iterations carried out (the default is 10). The iterations are terminated when the difference between two consecutive data estimates differs by less than Tol%. The default value of Tol is 1.
For a given model, the missing data is estimated as parameters so as to minimize the output prediction errors obtained from the reconstructed data. See Section 14.2 in Ljung (1999). Treating missing outputs as parameters is not the best approach from a statistical point of view, but is a good approximation in many cases.
When no model is given, the algorithm alternates between estimating missing data and estimating models, based on the current reconstruction.