Yes. Thank you; I should have quoted it. I suggest to remove this text or to add the word "not" at the beginning.
Arie On Sun, Oct 8, 2017 at 4:38 PM, Viechtbauer Wolfgang (SP) <wolfgang.viechtba...@maastrichtuniversity.nl> wrote: > Ah, I think you are referring to this part from ?lm: > > "(including the case that there are w_i observations equal to y_i and the > data have been summarized)" > > I see; indeed, I don't think this is what 'weights' should be used for (the > other part before that is correct). Sorry, I misunderstood the point you were > trying to make. > > Best, > Wolfgang > > -----Original Message----- > From: R-devel [mailto:r-devel-boun...@r-project.org] On Behalf Of Arie ten > Cate > Sent: Sunday, 08 October, 2017 14:55 > To: r-devel@r-project.org > Subject: [Rd] Discourage the weights= option of lm with summarized data > > Indeed: Using 'weights' is not meant to indicate that the same > observation is repeated 'n' times. As I showed, this gives erroneous > results. Hence I suggested that it is discouraged rather than > encouraged in the Details section of lm in the Reference manual. > > Arie > > ---Original Message----- > On Sat, 7 Oct 2017, wolfgang.viechtba...@maastrichtuniversity.nl wrote: > > Using 'weights' is not meant to indicate that the same observation is > repeated 'n' times. It is meant to indicate different variances (or to > be precise, that the variance of the last observation in 'x' is > sigma^2 / n, while the first three observations have variance > sigma^2). > > Best, > Wolfgang > > -----Original Message----- > From: R-devel [mailto:r-devel-boun...@r-project.org] On Behalf Of Arie ten > Cate > Sent: Saturday, 07 October, 2017 9:36 > To: r-devel@r-project.org > Subject: [Rd] Discourage the weights= option of lm with summarized data > > In the Details section of lm (linear models) in the Reference manual, > it is suggested to use the weights= option for summarized data. This > must be discouraged rather than encouraged. The motivation for this is > as follows. > > With summarized data the standard errors get smaller with increasing > numbers of observations. However, the standard errors in lm do not get > smaller when for instance all weights are multiplied with the same > constant larger than one, since the inverse weights are merely > proportional to the error variances. > > Here is an example of the estimated standard errors being too large > with the weights= option. The p value and the number of degrees of > freedom are also wrong. The parameter estimates are correct. > > n <- 10 > x <- c(1,2,3,4) > y <- c(1,2,5,4) > w <- c(1,1,1,n) > xb <- c(x,rep(x[4],n-1)) # restore the original data > yb <- c(y,rep(y[4],n-1)) > print(summary(lm(yb ~ xb))) > print(summary(lm(y ~ x, weights=w))) > > Compare with PROC REG in SAS, with a WEIGHT statement (like R) and a > FREQ statement (for summarized data). > > Arie > > ______________________________________________ > R-devel@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel ______________________________________________ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
