The model object can even store the data used for identification, in the
property `data`. Therefore, `plot(idmobj)` can plot not only the
transfer function, but also the measured data, and the error of
identification.

load bandpmod get(bkfit) version = 1.2 date = '22-Oct-1998 09:09:26' history = {[1x48 char]} data = [1x1 fiddata] variable = 's' num = [-4.0066e-18 -5.1034e-13 -9.2494e-11 2.7921e-09 1.5859e-05] denom = [-9.4278e-23 -2.3515e-19 -3.3573e-15 -5.1609e-12 -3.3479e-08 -2.3288e-05 -0.093552] representation = 'polynomial' freqvect = [16x1 double] fscale = 3835 delays = 0 covariance = [13x13 double] fitinfo = [18x1 double] >> set(bkfit) name: string version: version number, set by the system date: string (date + time) notes: string history: cell vector of strings data: optional, input data of estimation (tiddata or fiddata) algorithm: structure describing the algorithm userdata: user-defined variable: [ 'z^-1' | 's' | 'r' | 'w' ] representation: [ 'polynomial' | 'orthopol' ] num: cell array of numerators denom: cell array of denominators ntr: cell array of transient numerator polynomials Znum: array of weight vectors of numerator Zdenom: array of weight vectors of denominator Zntr: array of weight vectors of transient numerator freqvect: column vector or array, freqs x channels chnames: string cell array, length: channels chtypes: string cell array, [ 'input' | 'output' ] fscale: scalar, optimum scaling frequency delays: vector of delay values units: cell array of strings covariance: array of covariances fixedpar: nx2 array of fixed parameters fitinfo: cell array of information on fit