Robust fit of linear subspace through multidimensional data.
1 2 3 
X 
NxK 
constraint 
A If If If 
baselineChannel 
Index of channel toward which all other
channels are conform.
This argument is required if 
... 
Additional arguments accepted by 
aShift, Xmin 
For internal use only. 
This method uses reweighted principal component analysis (IWPCA) to fit a the nodel y_n = a + bx_n + eps_n where y_n, a, b, and eps_n are vector of the K and x_n is a scalar.
The algorithm is: For iteration i: 1) Fit a line L through the data close using weighted PCA with weights \{w_n\}. Let r_n = \{r_{n,1},...,r_{n,K}\} be the K principal components. 2) Update the weights as w_n < 1 / ∑_{2}^{K} (r_{n,k} + ε_r) where we have used the residuals of all but the first principal component. 3) Find the point a on L that is closest to the line D=(1,1,...,1). Similarily, denote the point on D that is closest to L by t=a*(1,1,...,1).
Returns a list
that contains estimated parameters and algorithm
details;
a 
A 
b 
A 
adiag 
If identifiability constraint 
eigen 
A KxK 
converged 

nbrOfIterations 
The number of iterations for the algorithm to converge, or zero if it did not converge. 
t0 
Internal parameter estimates, which contains no more information than the above listed elements. 
t 
Always 
Henrik Bengtsson
This is an internal method used by the calibrateMultiscan
()
and normalizeAffine
() methods.
Internally the function iwpca
() is used to fit a line
through the data cloud and the function distanceBetweenLines
() to
find the closest point to the diagonal (1,1,...,1).
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