Hi, when I calculate a linear regression with gsl_fit_linear() there are two different ways of obtaining the pvalue.
ESS <- sum((y-yestimate)^2) or direct use of "sumsq" (which is given back fy the function) SSR <- sum((yestimate-mean(y))^2) TSS <- sum((y-mean(y))^2) R^2 <- SSR/TSS or 1-ESS/TSS F-statistic: R^2/(p-1) --------- (1-R^2)/(n-p) where n is the number of elements and p the number of variables, normally p=2 so the term will be simplified to: f_stat = R^2/((1-R^2)/(n-2)) and the pvalue is: 1 - gsl_cdf_fdist_P (f_stat,1,nelem-2) Now my question: What changes here when I perform a weighted linear regression with gsl_fit_wlinear? The "sumsq" is already calculated by the function, but if I had to calculate the ESS manually then I would have to multiply the (y-yestimate) differences with the weights before applying the power of 2 and the sum. The same should apply to the TSS, doens't it? And then the rest of the calculation remains the same as for a nonweighted regression. But obviously there still HAS to be another difference in calculation. Because when I perform the same regression in R with lm(y~x,weights=weights) I DO get a different R2 value and thereby different f-statistic. And this is not a basic calculation error because my slope and intercept are identical in R. Any suggestions what I'am doing wrong? best regards, Benjamin Otto -- Pflichtangaben gemäß Gesetz über elektronische Handelsregister und Genossenschaftsregister sowie das Unternehmensregister (EHUG): Universitätsklinikum Hamburg-Eppendorf Körperschaft des öffentlichen Rechts Gerichtsstand: Hamburg Vorstandsmitglieder: Prof. Dr. Jörg F. Debatin (Vorsitzender) Dr. Alexander Kirstein Ricarda Klein Prof. Dr. Dr. Uwe Koch-Gromus _______________________________________________ Help-gsl mailing list [email protected] http://lists.gnu.org/mailman/listinfo/help-gsl
