Hi, Sundar: Thanks, Sundar. That should have been obvious to me. However, I hadn't used varFixed before, and evidently I thought about it for only 1 ms instead of the required 2. With that change, I get the same answers for all three.
Best Wishes, spencer Sundar Dorai-Raj wrote: > Hi, Spencer, > > For your call to gls you actually want: > > fit.gls.w <- gls(y~x, data=DF, weights=varFixed(~1/w)) > > HTH, > > --sundar > > Spencer Graves wrote: > >> In my tests, "gls" did NOT give the same answers as "lm" and "glm", >>and I don't know why; perhaps someone else will enlighten us both. I >>got the same answers from "lm" and "glm". Since you report different >>results, please supply a replicatable example. >> >> I tried the following: >>set.seed(1) >>DF <- data.frame(x=1:8, xf=rep(c("a", "b"), 4), >> y=rnorm(8), w=1:8, one=rep(1,8)) >>fit.lm.w <- lm(y~x, DF, weights=w) >>fit.glm.w <- glm(y~x, data=DF, weights=w) >>fit.gls.w <- gls(y~x, data=DF, >> weights=varFixed(~w)) >> >> >> >>>coef(fit.lm.w) >> >>(Intercept) x >> -0.2667521 0.0944190 >> >> >>>coef(fit.glm.w) >> >>(Intercept) x >> -0.2667521 0.0944190 >> >> >>>coef(fit.gls.w) >> >>(Intercept) x >> -0.5924727 0.1608727 >> >> I also tried several variants of this. I know this does not answer >>your questions, but I hope it will contribute to an answer. >> >> spencer graves >> >>Goeland wrote: >> >> >> >>>Dear r-users, >>> >>>Can anyone explain exactly the difference between Weights options in lm glm >>>and gls? >>> >>>I try the following codes, but the results are different. >>> >>> >>> >>> >>> >>>>lm1 >>> >>> >>>Call: >>>lm(formula = y ~ x) >>> >>>Coefficients: >>>(Intercept) x >>> 0.1183 7.3075 >>> >>> >>> >>> >>>>lm2 >>> >>> >>>Call: >>>lm(formula = y ~ x, weights = W) >>> >>>Coefficients: >>>(Intercept) x >>> 0.04193 7.30660 >>> >>> >>> >>> >>>>lm3 >>> >>> >>>Call: >>>lm(formula = ys ~ Xs - 1) >>> >>>Coefficients: >>> Xs Xsx >>>0.04193 7.30660 >>> >>>Here ys= y*sqrt(W), Xs<- sqrt(W)*cbind(1,x) >>> >>>So we can see weights here for lm means the scale for X and y. >>> >>>But for glm and gls I try >>> >>> >>> >>> >>>>glm1 >>> >>> >>>Call: glm(formula = y ~ x) >>> >>>Coefficients: >>>(Intercept) x >>> 0.1183 7.3075 >>> >>>Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual >>>Null Deviance: 1049000 >>>Residual Deviance: 28210 AIC: 7414 >>> >>> >>> >>>>glm2 >>> >>> >>>Call: glm(formula = y ~ x, weights = W) >>> >>>Coefficients: >>>(Intercept) x >>> 0.1955 7.3053 >>> >>>Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual >>>Null Deviance: 1548000 >>>Residual Deviance: 44800 AIC: 11670 >>> >>> >>> >>>>glm3 >>> >>> >>>Call: glm(formula = y ~ x, weights = 1/W) >>> >>>Coefficients: >>>(Intercept) x >>> 0.03104 7.31033 >>> >>>Degrees of Freedom: 1242 Total (i.e. Null); 1241 Residual >>>Null Deviance: 798900 >>>Residual Deviance: 19900 AIC: 5285 >>> >>> >>> >>> >>>>glm4 >>> >>> >>>Call: glm(formula = ys ~ Xs - 1) >>> >>>Coefficients: >>> Xs Xsx >>>2.687 6.528 >>> >>>Degrees of Freedom: 1243 Total (i.e. Null); 1241 Residual >>>Null Deviance: 4490000 >>>Residual Deviance: 506700 AIC: 11000 >>> >>>With weights, the glm did not give the same results as lm why? >>> >>>Also for gls, I use varFixed here. >>> >>> >>> >>> >>>>gls3 >>> >>>Generalized least squares fit by REML >>> Model: y ~ x >>> Data: NULL >>> Log-restricted-likelihood: -3737.392 >>> >>>Coefficients: >>>(Intercept) x >>>0.03104214 7.31032540 >>> >>>Variance function: >>>Structure: fixed weights >>>Formula: ~W >>>Degrees of freedom: 1243 total; 1241 residual >>>Residual standard error: 4.004827 >>> >>> >>> >>>>gls4 >>> >>>Generalized least squares fit by REML >>> Model: ys ~ Xs - 1 >>> Data: NULL >>> Log-restricted-likelihood: -5500.311 >>> >>>Coefficients: >>> Xs Xsx >>>2.687205 6.527893 >>> >>>Degrees of freedom: 1243 total; 1241 residual >>>Residual standard error: 20.20705 >>> >>>We can see the relation between glm and gls with weight as what >>> >>>I think, but what's the difference between lm wit gls and glm? why? >>> >>>Thanks so much.! >>> >>>Goeland >>> >>> >>> >>>Goeland >>>[EMAIL PROTECTED] >>>2006-03-16 >>> >>> >>> >>>------------------------------------------------------------------------ >>> >>>______________________________________________ >>>R-help@stat.math.ethz.ch mailing list >>>https://stat.ethz.ch/mailman/listinfo/r-help >>>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >> >> >> >>------------------------------------------------------------------------ >> >>______________________________________________ >>R-help@stat.math.ethz.ch mailing list >>https://stat.ethz.ch/mailman/listinfo/r-help >>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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