Description Usage Arguments Details Value Author(s) See Also
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 re-weighted 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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