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...lysis, princomp and prcomp. I don't really know the difference; the only thing I know is that when the sample size < number of variable, only prcomp will work. Could someone tell me the difference or where I can find easy-to-read reference? To access the first PC using princomp: Mpca<-princomp(M, cor=T) Mpca$scores[,1] How can I access the first PC using prcomp? Mpca<-prcomp(M) Is there an option for "cor=T"? In case where both functions work, will the results be the same? Thanks, Miao [[alternative HTML version deleted]]

...than vector as explanatory variables (2)I don't know how to do a forecast with the estimation results of dynlm properly. In lm model, function "predict.lm" can do it. For the case of first principal component (In order to accentuate the main problem, I have simplify the codes): Mpca<-prcomp(M1, center=TRUE, scale =TRUE) # M1 is the data matrix of explanatory variables Mpca1st<-Mpca$x[,1] # first principle component X<-as.matrix(Mpca1st) model<-dynlm(as.ts(y[(h+1):t]) ~ L(as.ts(X[1:(t-h)]), 0:i) + L(as.ts(z[1:(t-h)]),0:j)) # y, X, z are a zoo objects def...