search for: devainc

...lm.object 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 Bielef...

...rsion 2.9.0) coxph.fit0 <- coxph(y ~ z2_ + cluster(as.factor(keys))+ strata(stratvar_), method="breslow" ,robust=T ) coxph.fit1 <- coxph(y ~ z_ + cluster(as.factor(keys))+ strata(stratvar_), method="breslow" ,robust=T ) # marker and covariates # Analysis of Devaince table coxph.aov <- anova(coxph.fit0 , coxph.fit1, test="Chisq") In the single models coxph.fit0 and coxph.fit1 I can use a robust variance estimation. It seems that the function anova don't use a robust estimation for the analysis of deviance. My question is, how can I use robus...

hi, i'm using gam() function from package mgcv with default option (edf estimated by GCV). >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) >SG=summary(G) Formula: y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 Approximate significance of smooth

...(y ~ z2_ + cluster(as.factor(keys))+ > strata(stratvar_), > method="breslow" ,robust=T ) > > coxph.fit1 <- coxph(y ~ z_ + cluster(as.factor(keys))+ > strata(stratvar_), > method="breslow" ,robust=T ) # marker and covariates > > # Analysis of Devaince table > coxph.aov <- anova(coxph.fit0 , coxph.fit1, test="Chisq") > > In the single models coxph.fit0 and coxph.fit1 I can use a robust > variance estimation. > It seems that the function anova don't use a robust estimation for > the analysis of deviance. &gt...