I am looking for a way to perform weighted total least squares in R. I know one can use PCA for this as described nicely in the following post. How to perform orthogonal regression (total least squares) via PCA?
However, I need a weighted version of total least squares, i.e. I want to account for measurement error in my data, where the error $(\Delta x_i, \Delta y_i)$ can be different for each data point $(x_i, y_i)$. Any suggestions? Specifically, I want to do the following:
x = rnorm(10,0,2) x.se = rnorm(10,0,0.7) y = 20*x y.se = rnorm(10,0,1) r <- prcomp( ~ x + y ) slope <- r$rotation[2,1] / r$rotation[1,1] intercept <- r$center - slope*r$center
However, here I am not accounting for the varying measurement errors in $x$ and $y$. Is there any R package which I can use to account for the