Hello,

after (1) getting independent components from the (200*20) data matrix X_t   by 
applying

PearsonICA(data matrix, n.comp = 2, row.norm = FALSE, maxit = 200, tol = 1e-04, 
border.base = c(2.6, 4), border.slope = c(0, 1), verbose = FALSE, w.init = 
NULL, na.rm = FALSE, whitening.only = FALSE, PCA.only = FALSE)

I am (2) trying to let the ICA-components converge (abs($S_1-$S_0)<0) in the 
relation

X_t=Lambda*F+Lambda1*Y

with F as estimated factors obtained from the observed time series X_t 
(200*1-vector)  and Y_t(2*1-vector). The goal is to obtain final estimate of F 
and then to estimate VAR in F and Y, FAVAR.

My version is:

#multiplicate the rows (20 time periods) of the column-ordered by variables 
matrix X_ with the column of the two independent components from the step (1), 
get the initial #estimate F_0

#print (F_0)

while (abs(F_0-F_1)<0,01) {
regression<-lm(X_t~F_0,Y_t) 
#here is the place for beta_y_t, obtained regression coefficient relating to Y_t
beta_y_t<-regression$coefficients[2] 
X_1_t<-X_t-beta_y_t*Y_t #new data
F_0<-PearsonICA(X_1_t, n.comp = 2, row.norm = FALSE, maxit = 200, tol = 1e-04, 
border.base = c(2.6, 4), border.slope = c(0, 1), verbose = FALSE, w.init = 
NULL, na.rm = FALSE, whitening.only = FALSE, PCA.only = FALSE)
F_1<-F_0
}
F_1

Thanks in advance
Viktor

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