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: fdacpcaK Description: perform the common estimation for functional PCA, using simultaneous diagonalization

 Usage: {fpcaresult,values,varprop,scores} = fdacpcaK(CovC,N,basisfd,lambda,lfd,npc,orthonorm) Input: CovC p x p x k array of covariances of coefficients N k x 1 vector of weight, usually number of observations in each group basisfd list, functional basis (fdbasis object) 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" 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 data (fd) object values npc x 2 matrix, eigenvalues varprop npc x 2 matrix, variance proportions

Example:
```library("plot")
library("fda")
axeson()
y =t[,1:17]
y2=t[,18:35]
tvec=#(1:365)/365
fdb = createfdbasis("fourier", #(0,1),9,1)
fdtempf31=data2fd(y,tvec,fdb)
fd2tempf31=data2fd(y2,tvec,fdb)
covfdtempf31=cov(fdtempf31.coef')
covfd2tempf31=cov(fd2tempf31.coef')
covC31temp=stack(covfdtempf31,covfd2tempf31)
N=17|17 ; weights
cpc=fdacpcaK(covC31temp,N,fdb,0.000001,2,4)
Tempdi=createdisplay(1,1)
grtempfc=grfd(cpc.fcpcaKresult,grid(0,1/100,100),0,#(0,1,2,3))
show(Tempdi,1,1,grtempfc)

```
Result:
```Plots the common estimation of first and second part of
the temperature data set and prints the eigenvalues for both groups
```

Author: M. Benko 20041208 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006