Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

 Quantlet: fdaspca Description: performs the smoothed functional PCA

 Usage: {fpcaresult,values,varprop,scores} = fdaspca(fdobject,lambda,lfd,npc{,norm}) Input: fdobject list, functional (fd) object with n repetitions lambda scalar, smoothing parameter lfd list, LDO object, linear differential opereator npc scalar, number of eigenfunctions, default = 4 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 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

Note:
orthogonalization is usefull just for lambda>0, for lambda=0 is orthogonality quaranteed

Example:
```library("plot")
library("fda")
axeson()
tvec=#(1:365)/365
fdb = createfdbasis("fourier", #(0,1),31,1)
fdtempf31=data2fd(y,tvec,fdb)
fdapc=fdaspca(fdtempf31,0.0,2,4)
Tempdi=createdisplay(1,1)
grtempf=grfd(fdapc.fpcaresult,grid(0,1/100,100),0,#(0,1,2,3))
show(Tempdi,1,1,grtempf)

```
Result:
```first 4 eigenfunctions are plotted
```

Author: M. Benko 20041205 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006