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

[peter dalgaard]

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 correct results from things like

y <- c(0,1)
w <- c(49,51)
glm(y~1, weights=w, family=binomial)

-pd
 


> On 9 Oct 2017, at 07:58 , Arie ten Cate <arietencate at gmail.com> wrote:
> 
> 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.viechtbauer at 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-bounces at r-project.org] On Behalf Of Arie ten Cate
>> Sent: Sunday, 08 October, 2017 14:55
>> To: r-devel at 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.viechtbauer at 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-bounces at r-project.org] On Behalf Of Arie ten Cate
>> Sent: Saturday, 07 October, 2017 9:36
>> To: r-devel at 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
>> 
>> ______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-devel
> 
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-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
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