Re: [Rd] Discourage the weights= option of lm with summarized data

> On 3 Dec 2017, at 16:31 , Arie ten Cate wrote: > > Peter, > > This is a highly structured text. Just for the discussion, I separate > the building blocks, where (D) and (E) and (F) are new: > > BEGIN OF TEXT > > (A) > > Non-‘NULL’ ‘weights’ can

Re: [Rd] Discourage the weights= option of lm with summarized data

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT (A) Non-‘NULL’ ‘weights’ can be used to indicate that different observations have different variances (with the values in

Re: [Rd] Discourage the weights= option of lm with summarized data

My local R-devel version now has (in ?lm) Non-‘NULL’ ‘weights’ can be used to indicate that different observations have different variances (with the values in ‘weights’ being inversely proportional to the variances); or equivalently, when the elements of ‘weights’ are

Re: [Rd] Discourage the weights= option of lm with summarized data

It's on my todo list (for R-devel, it is not _that_ important), other things just keep taking priority... -pd > On 28 Nov 2017, at 09:29 , Arie ten Cate wrote: > > Since the three posters agree (only) that there is a bug, I propose to > file it as a bug, which is the

Re: [Rd] Discourage the weights= option of lm with summarized data

Since the three posters agree (only) that there is a bug, I propose to file it as a bug, which is the least we can do now. There is more to it: the only other case of a change in the Reference Manual which I know of, is also about the weights option! This is in coxph. The Reference Manual version

Re: [Rd] Discourage the weights= option of lm with summarized data

OK. We have now three suggestions to repair the text: - remove the text - add "not" at the beginning of the text - add at the end of the text a warning; something like: "Note that in this case the standard estimates of the parameters are in general not correct, and hence also the t values

Re: [Rd] Discourage the weights= option of lm with summarized data

AFAIR, it is a little more subtle than that. If you have replication weights, then the estimates are right, it is "just" that the SE from summary.lm() are wrong. Somehow, the text should reflect this. It is of some importance when you put glm() into the mix, because you can in fact get

Re: [Rd] Discourage the weights= option of lm with summarized data

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) wrote: > Ah, I think you are referring to this part from ?lm: > >

Re: [Rd] Discourage the weights= option of lm with summarized data

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

[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

Re: [Rd] Discourage the weights= option of lm with summarized data

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

[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