i am trying to duplicate R's computation of standard errors but having some trouble. i loaded some data into R and ran summary(lm(y~x1+x2+x3+0, data=data)), but i am not sure how the "Std. Error" values are computed.
let y be the nx1 vector of dependent variables and X be the nx3 matrix of independent variables. let T(.) denote the transpose of a matrix/vector, and let I(.) denote the inverse of a square matrix. then i'm able to correctly compute the coefficients and residual standard error using the following formulas: beta = I(T(X)*X) * y resid err = sqrt(T(y)*y - 2*T(beta)*y + T(beta)*T(X)*X*beta) / sqrt(n - 3) i then try to compute the coefficient standard errors via: coeff err(i) = sqrt(I(T(X)*X)[i,i]) / (resid err) where .[i,i] means the ith entry on the diagonal of the given matrix. however, doing this gives values that are off by a multiplicative factor. the factor is the same for all coefficients, but it is not 1, and the value varies for different data sets. what is this term? thanks, Rnewb -- View this message in context: http://n4.nabble.com/how-does-R-compute-Std-Error-s-tp1588537p1588537.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
