Suppose one wanted to consider random variables X_1,...X_n and from each
subtract off the piece which is correlated with the previous variables in the
list. i.e. make new variables Z_i so that Z_1=X_1 and
Z_i=X_i-cov(X_i,Z_1)Z_1/var(Z_1)-...- cov(X_i,Z__{i-1})Z__{i-1}/var(Z_{i-1}) I
have code to do this but I keep getting a "non-conformable array" error in the
line with the covariance. Does anyone have any suggestions? Here is my code:
gov=read.table(file.choose(), sep="\t",header=T)
gov1=gov[3:length(gov[1,])]
n_indices=length(names(gov1))
x=data.matrix(gov1)
v=x
R=matrix(rep(0,length(x[,1])*length(x[1,])),length(x[,1]))
for(j in 1:n_indices){
u=matrix(rep(0,length(v[,1])),length(v[,1]))
for(i in 1:j-1){
u = u+cov(v[,j],v[,i])*v[,i]/var(v[,i]) #(error here)
}
v[,j]=v[,j]-u
}
Thanks,
Andrew
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