Usage: |
{fpcaresult,values,varprop,scores} = fdaspca(fdobject,lambda,lfd,npc{,norm})
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Input: |
| fdobject | list, functional (fd) object with n repetitions
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| lambda | scalar, smoothing parameter
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| lfd | list, LDO object, linear differential opereator
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| npc | scalar, number of eigenfunctions, default = 4
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| norm | string, normalization type, if norm=="orthonorm" fpcaresult.coef are orthonormalized (with respect to the basis penalty matrix), if norm=="norm" the coefficients are renormalized to norm=1, default is no normalization
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Output: |
| fpcaresult | list, functional (fd) object |
| values | vector, npc x 1, eigenvalues |
| varprop | vector, npc x 1, variance proportions |
| scores | n x npc matrix, principal scores |