search for: residi

...assign( names( theta )[i], val ) # gags here cause I can't get the names... } ## resids = eval( lhs ) - eval( rhs ) for( i in length( eqns ) ) { lhs[[i]] <- eval( formula( eqns[[i]] )[2] ) rhs[[i]] <- eval( formula( eqns[[i]] )[3] ) residi[[i]] <- lhs[[i]] - rhs[[i]] r <- rbind( r, as.matrix( residi[[i]] ) ) } ## blah, blah, blah.... knls <- obj } print( "calling nlstest" ) demand2 <- q ~ d0 + d1 * p + d2 * d supply2 <- q ~ s0 + s1 * p + s2 * f + s3 * a system2 <- list( demand2, s...

...assign( names( theta )[i], val ) # gags here cause I can't get the names... } ## resids = eval( lhs ) - eval( rhs ) for( i in length( eqns ) ) { lhs[[i]] <- eval( formula( eqns[[i]] )[2] ) rhs[[i]] <- eval( formula( eqns[[i]] )[3] ) residi[[i]] <- lhs[[i]] - rhs[[i]] r <- rbind( r, as.matrix( residi[[i]] ) ) } ## blah, blah, blah.... knls <- obj } print( "calling nlstest" ) demand2 <- q ~ d0 + d1 * p + d2 * d supply2 <- q ~ s0 + s1 * p + s2 * f + s3 * a system2 <- list( demand2, s...

...x with plot(x) are calculated as r <- residuals(x) s <- sqrt(deviance(x)/df.residual(x)) w <- weights(x) hii <- lm.influence(x)$hat r.w <- if (is.null(w)) r else (sqrt(w) * r) rs <- r.w/(s * sqrt(1 - hii)) This implies that, for example, for binomial B(ni,pi) data the devaince residials (which are just r) are weighted not only with sqrt(1-hii), but also with 1/sqrt(ni) and s, leading to absurd values. As a result all leverage/outlier diagnostics is absolutly wrong. Am I right and this should be reported as a bug? Many thanks, Tatyana -- Tatyana Krivobokova Bielefeld Unive...