Get mean and std from sfit object - matlab

Question

After loading, a pre-fitted surface (sfit) model is something like in the following figure.
Question: How can I obtain the precise mean/std (not by tedious copying from the output) of x/y data from the sfit object?
Remarks:
I am able to get all the coefficients by calling its coeffnames/coeffvalues APIs. However it seems there's no similar API for the mean/std.
The original data that the sfit model is fit from is not accessible currently. So the method relying on the original data is not applicable.

Answer 1

Looking at the source of the sfit class, it turns out that the means and standard deviations are stored in private properties meanx, meany, stdx, stdy. The fact that these are private makes the job non-trivial, but thanks to Yair Altman we know that calling struct() on a class usually reveals all its goodness.
Using the slightly modified example from the sfit documentation,
x = 3 - 6 * rand( 49, 1 );
y = 3 - 6 * rand( 49, 1 );
z = peaks( x, y );
sf = fit( [x, y], z, 'poly32', 'normalize', 'on');
here's what we see:
>> sf
Linear model Poly32:
sf(x,y) = p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 + p30*x^3 +
p21*x^2*y + p12*x*y^2
where x is normalized by mean -0.3736 and std 1.887
and where y is normalized by mean -0.04893 and std 1.644
Coefficients (with 95% confidence bounds):
p00 = 0.4227 (-0.3731, 1.218)
p10 = 1.764 (0.5627, 2.965)
p01 = 1.313 (0.7715, 1.855)
p20 = -0.1054 (-0.6496, 0.4389)
p11 = 0.4627 (0.03944, 0.8859)
p02 = 0.1898 (-0.2443, 0.6239)
p30 = -0.6345 (-1.247, -0.02209)
p21 = -0.8263 (-1.32, -0.3327)
p12 = -0.4908 (-1.011, 0.02911)
>> sf_struct=struct(sf)
Warning: Calling STRUCT on an object prevents the object from hiding its implementation details and should thus be avoided. Use DISP or
DISPLAY to see the visible public details of an object. See 'help struct' for more information.
sf_struct =
version: 2
fCoeffValues: {[0.4227] [1.7639] [1.3130] [-0.1054] [0.4627] [0.1898] [-0.6345] [-0.8263] [-0.4908]}
fProbValues: {1x0 cell}
sse: 59.5574
dfe: 40
rinv: [9x9 double]
activebounds: [9x1 logical]
meanx: -0.3736
meany: -0.0489
stdx: 1.8875
stdy: 1.6441
xlim: [-2.8236 2.8090]
ylim: [-2.7585 2.6763]
fType: 'poly32'
fTypename: 'Poly32'
fCategory: 'library'
defn: 'p00 + p10*x + p01*y + p20*x^2 + p11*x*y + p02*y^2 + p30*x^3 + p21*x^2*y + p12*x*y^2'
fFeval: 1
expr: #polySurface
Adefn: {}
Aexpr: {}
linear: 1
derexpr: #polySurfaceDerivative
intexpr: []
args: [11x3 char]
isEmpty: 0
numArgs: 11
numCoeffs: 9
assignCoeff: [1x234 char]
assignData: ' x = FITTYPE_INPUTS_{10}; y = FITTYPE_INPUTS_{11};'
assignProb: ''
indep: [2x1 char]
depen: 'z'
coeff: [9x3 char]
prob: ''
fConstants: {[3] [2]}
fNonlinearcoeffs: []
fFitoptions: [1x1 curvefit.llsqoptions]
fStartpt: []
>> [sf_struct.meanx, sf_struct.meany, sf_struct.stdx, sf_struct.stdy]
ans =
-0.3736 -0.0489 1.8875 1.6441
At least in R2012b the above works.