

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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
Sent: Saturday, 07 October, 2017 9:36
To: [hidden email]
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],n1)) # restore the original data
yb < c(y,rep(y[4],n1))
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
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
Sent: Sunday, 08 October, 2017 14:55
To: [hidden email]
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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
Sent: Saturday, 07 October, 2017 9:36
To: [hidden email]
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],n1)) # restore the original data
yb < c(y,rep(y[4],n1))
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
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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)
< [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
> Sent: Sunday, 08 October, 2017 14:55
> To: [hidden email]
> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
> Sent: Saturday, 07 October, 2017 9:36
> To: [hidden email]
> 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],n1)) # restore the original data
> yb < c(y,rep(y[4],n1))
> 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
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rdevel______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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 < [hidden email]> 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)
> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>> Sent: Sunday, 08 October, 2017 14:55
>> To: [hidden email]
>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>> Sent: Saturday, 07 October, 2017 9:36
>> To: [hidden email]
>> 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],n1)) # restore the original data
>> yb < c(y,rep(y[4],n1))
>> 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
>>
>> ______________________________________________
>> [hidden email] mailing list
>> https://stat.ethz.ch/mailman/listinfo/rdevel>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rdevel
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [hidden email] Priv: [hidden email]
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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 and the p value.
Also the number of degrees of freedom is not correct. (The parameter
values are correct.)"
A remark about the glm example: the Reference manual says: "For a
binomial GLM prior weights are used to give the number of trials when
the response is the proportion of successes ....". Hence in the
binomial case the weights are frequencies.
With y < 0.51 and w < 100 you get the same result.
Arie
On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
> 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 < [hidden email]> 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)
>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>> Sent: Sunday, 08 October, 2017 14:55
>>> To: [hidden email]
>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>> Sent: Saturday, 07 October, 2017 9:36
>>> To: [hidden email]
>>> 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],n1)) # restore the original data
>>> yb < c(y,rep(y[4],n1))
>>> 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
>>>
>>> ______________________________________________
>>> [hidden email] mailing list
>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>
>> ______________________________________________
>> [hidden email] mailing list
>> https://stat.ethz.ch/mailman/listinfo/rdevel>
> 
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Office: A 4.23
> Email: [hidden email] Priv: [hidden email]
>
>
>
>
>
>
>
>
>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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 3.0.0 (2013) says about coxph:
" ... If weights is a vector of integers, then the estimated
coefficients are equivalent to estimating the model from data with the
individual cases replicated as many times as indicated by weights."
This is not true, as can be seen from the following code, which uses
the data from the first example in the Reference Manual of coxph:
library(survival)
print(df1 < as.data.frame(list(
time=c(4,3,1,1,2,2,3),
status=c(1,1,1,0,1,1,0),
x=c(0,2,1,1,1,0,0),
sex=c(0,0,0,0,1,1,1)
)))
print(w < rep(2,7))
print(coxph(Surv(time,status) ~ x + strata(sex),data=df1,weights=w))
# manually doubling the data:
print(df2 < rbind(df1,df1))
print(coxph(Surv(time,status) ~ x + strata(sex), data=df2))
This should not come as a surprise, since with coxph the computation
of the likelihood (given the parameters) for a single observation uses
also the other observations.
This bug has been repaired. The present Reference Manual of coxph says
that the weights option specifies a vector of case weights, to which
is added only: "For a thorough discussion of these see the book by
Therneau and Grambsch."
Let us repair the other bug also.
Arie
On Thu, Oct 12, 2017 at 1:48 PM, Arie ten Cate < [hidden email]> wrote:
> 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 and the p value.
> Also the number of degrees of freedom is not correct. (The parameter
> values are correct.)"
>
> A remark about the glm example: the Reference manual says: "For a
> binomial GLM prior weights are used to give the number of trials when
> the response is the proportion of successes ....". Hence in the
> binomial case the weights are frequencies.
> With y < 0.51 and w < 100 you get the same result.
>
> Arie
>
> On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
>> 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 < [hidden email]> 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)
>>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>> Sent: Sunday, 08 October, 2017 14:55
>>>> To: [hidden email]
>>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>> Sent: Saturday, 07 October, 2017 9:36
>>>> To: [hidden email]
>>>> 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],n1)) # restore the original data
>>>> yb < c(y,rep(y[4],n1))
>>>> 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
>>>>
>>>> ______________________________________________
>>>> [hidden email] mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>
>>> ______________________________________________
>>> [hidden email] mailing list
>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>
>> 
>> Peter Dalgaard, Professor,
>> Center for Statistics, Copenhagen Business School
>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>> Phone: (+45)38153501
>> Office: A 4.23
>> Email: [hidden email] Priv: [hidden email]
>>
>>
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


It's on my todo list (for Rdevel, it is not _that_ important), other things just keep taking priority...
pd
> On 28 Nov 2017, at 09:29 , Arie ten Cate < [hidden email]> wrote:
>
> 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 3.0.0 (2013) says about coxph:
>
> " ... If weights is a vector of integers, then the estimated
> coefficients are equivalent to estimating the model from data with the
> individual cases replicated as many times as indicated by weights."
>
> This is not true, as can be seen from the following code, which uses
> the data from the first example in the Reference Manual of coxph:
>
> library(survival)
> print(df1 < as.data.frame(list(
> time=c(4,3,1,1,2,2,3),
> status=c(1,1,1,0,1,1,0),
> x=c(0,2,1,1,1,0,0),
> sex=c(0,0,0,0,1,1,1)
> )))
> print(w < rep(2,7))
> print(coxph(Surv(time,status) ~ x + strata(sex),data=df1,weights=w))
> # manually doubling the data:
> print(df2 < rbind(df1,df1))
> print(coxph(Surv(time,status) ~ x + strata(sex), data=df2))
>
> This should not come as a surprise, since with coxph the computation
> of the likelihood (given the parameters) for a single observation uses
> also the other observations.
>
> This bug has been repaired. The present Reference Manual of coxph says
> that the weights option specifies a vector of case weights, to which
> is added only: "For a thorough discussion of these see the book by
> Therneau and Grambsch."
>
> Let us repair the other bug also.
>
> Arie
>
> On Thu, Oct 12, 2017 at 1:48 PM, Arie ten Cate < [hidden email]> wrote:
>> 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 and the p value.
>> Also the number of degrees of freedom is not correct. (The parameter
>> values are correct.)"
>>
>> A remark about the glm example: the Reference manual says: "For a
>> binomial GLM prior weights are used to give the number of trials when
>> the response is the proportion of successes ....". Hence in the
>> binomial case the weights are frequencies.
>> With y < 0.51 and w < 100 you get the same result.
>>
>> Arie
>>
>> On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
>>> 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 < [hidden email]> 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)
>>>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>> Sent: Sunday, 08 October, 2017 14:55
>>>>> To: [hidden email]
>>>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>> Sent: Saturday, 07 October, 2017 9:36
>>>>> To: [hidden email]
>>>>> 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],n1)) # restore the original data
>>>>> yb < c(y,rep(y[4],n1))
>>>>> 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
>>>>>
>>>>> ______________________________________________
>>>>> [hidden email] mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>>
>>>> ______________________________________________
>>>> [hidden email] mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>
>>> 
>>> Peter Dalgaard, Professor,
>>> Center for Statistics, Copenhagen Business School
>>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>>> Phone: (+45)38153501
>>> Office: A 4.23
>>> Email: [hidden email] Priv: [hidden email]
>>>
>>>
>
> ______________________________________________
> [hidden email] mailing list
> https://stat.ethz.ch/mailman/listinfo/rdevel
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [hidden email] Priv: [hidden email]
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


My local Rdevel 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 positive integers
w_i, that each response y_i is the mean of w_i unitweight
observations (including the case that there are w_i observations
equal to y_i and the data have been summarized). However, in the
latter case, notice that withingroup variation is not used.
Therefore, the sigma estimate and residual degrees of freedom may
be suboptimal; in the case of replication weights, even wrong.
Hence, standard errors and analysis of variance tables should be
treated with care.
OK?
pd
> On 12 Oct 2017, at 13:48 , Arie ten Cate < [hidden email]> wrote:
>
> 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 and the p value.
> Also the number of degrees of freedom is not correct. (The parameter
> values are correct.)"
>
> A remark about the glm example: the Reference manual says: "For a
> binomial GLM prior weights are used to give the number of trials when
> the response is the proportion of successes ....". Hence in the
> binomial case the weights are frequencies.
> With y < 0.51 and w < 100 you get the same result.
>
> Arie
>
> On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
>> 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 < [hidden email]> 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)
>>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>> Sent: Sunday, 08 October, 2017 14:55
>>>> To: [hidden email]
>>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>> Sent: Saturday, 07 October, 2017 9:36
>>>> To: [hidden email]
>>>> 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],n1)) # restore the original data
>>>> yb < c(y,rep(y[4],n1))
>>>> 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
>>>>
>>>> ______________________________________________
>>>> [hidden email] mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>
>>> ______________________________________________
>>> [hidden email] mailing list
>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>
>> 
>> Peter Dalgaard, Professor,
>> Center for Statistics, Copenhagen Business School
>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>> Phone: (+45)38153501
>> Office: A 4.23
>> Email: [hidden email] Priv: [hidden email]
>>
>>
>>
>>
>>
>>
>>
>>
>>

Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [hidden email] Priv: [hidden email]
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


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 ‘weights’
being inversely proportional to the variances);
(B)
or equivalently, when the elements of ‘weights’ are positive integers
w_i, that each response y_i is the mean of w_i unitweight
observations
(C)
(including the case that there are w_i observations equal to y_i and
the data have been summarized).
(D)
However, in the latter case, notice that withingroup variation is not
used. Therefore, the sigma estimate and residual degrees of freedom
may be suboptimal;
(E)
in the case of replication weights, even wrong.
(F)
Hence, standard errors and analysis of variance tables should be
treated with care.
END OF TEXT 
I don't understand (D), partly because it is unclear to me whether (D)
refers to (C) or to (B)+(C):
If (D) refers only to (C), as the reader might automatically think
with the repetition of the word "case", then it is unclear to me to
what block does (E) refer.
If, on the other hand, (D) refers to (B)+(C) then (E) probably
refers to (C) and then I suggest to make this more clear by replacing
"in the case of replication weights" in (E) by "in the case of
summarized data".
I suggest to change "even wrong" in (E) into the more downtoearth "wrong".
(For the record: I prefer something like my original explanation of
the problem with (C), instead of (D)+(E)+(F):
"With summarized data the standard errors get smaller with
increasing numbers of observations w_i. However, when for instance all
w_i are multiplied with the same constant larger than one, the
reported standard errors do not get smaller since the w_i are defined
apart from an arbitrary positive multiplicative constant. Hence the
reported standard errors tend to be too large and the reported t
values and the reported number of significance stars too small.
Obviously, also the reported number of observations and the reported
number of degrees of freedom are too small."
Note that with heteroskedasticity, _the_ residual standard error
has no meaning.)
Finally, about the original text: (B) and (C) mention only y_i, not
x_i, while this is about entire observations. Maybe this can remedied
also?
Arie
On Tue, Nov 28, 2017 at 1:01 PM, peter dalgaard < [hidden email]> wrote:
> My local Rdevel 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 positive integers
> w_i, that each response y_i is the mean of w_i unitweight
> observations (including the case that there are w_i observations
> equal to y_i and the data have been summarized). However, in the
> latter case, notice that withingroup variation is not used.
> Therefore, the sigma estimate and residual degrees of freedom may
> be suboptimal; in the case of replication weights, even wrong.
> Hence, standard errors and analysis of variance tables should be
> treated with care.
>
> OK?
>
>
> pd
>
>
>> On 12 Oct 2017, at 13:48 , Arie ten Cate < [hidden email]> wrote:
>>
>> 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 and the p value.
>> Also the number of degrees of freedom is not correct. (The parameter
>> values are correct.)"
>>
>> A remark about the glm example: the Reference manual says: "For a
>> binomial GLM prior weights are used to give the number of trials when
>> the response is the proportion of successes ....". Hence in the
>> binomial case the weights are frequencies.
>> With y < 0.51 and w < 100 you get the same result.
>>
>> Arie
>>
>> On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
>>> 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 < [hidden email]> 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)
>>>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>> Sent: Sunday, 08 October, 2017 14:55
>>>>> To: [hidden email]
>>>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>> Sent: Saturday, 07 October, 2017 9:36
>>>>> To: [hidden email]
>>>>> 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],n1)) # restore the original data
>>>>> yb < c(y,rep(y[4],n1))
>>>>> 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
>>>>>
>>>>> ______________________________________________
>>>>> [hidden email] mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>>
>>>> ______________________________________________
>>>> [hidden email] mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>
>>> 
>>> Peter Dalgaard, Professor,
>>> Center for Statistics, Copenhagen Business School
>>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>>> Phone: (+45)38153501
>>> Office: A 4.23
>>> Email: [hidden email] Priv: [hidden email]
>>>
>
> 
> Peter Dalgaard, Professor,
> Center for Statistics, Copenhagen Business School
> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
> Phone: (+45)38153501
> Office: A 4.23
> Email: [hidden email] Priv: [hidden email]
______________________________________________
[hidden email] mailing list
https://stat.ethz.ch/mailman/listinfo/rdevel


> On 3 Dec 2017, at 16:31 , Arie ten Cate < [hidden email]> 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 be used to indicate that different
> observations have different variances (with the values in ‘weights’
> being inversely proportional to the variances);
>
> (B)
>
> or equivalently, when the elements of ‘weights’ are positive integers
> w_i, that each response y_i is the mean of w_i unitweight
> observations
>
> (C)
>
> (including the case that there are w_i observations equal to y_i and
> the data have been summarized).
>
> (D)
>
> However, in the latter case, notice that withingroup variation is not
> used. Therefore, the sigma estimate and residual degrees of freedom
> may be suboptimal;
>
> (E)
>
> in the case of replication weights, even wrong.
>
> (F)
>
> Hence, standard errors and analysis of variance tables should be
> treated with care.
>
> END OF TEXT 
>
> I don't understand (D), partly because it is unclear to me whether (D)
> refers to (C) or to (B)+(C):
B, including C, is "the latter case".
> If (D) refers only to (C), as the reader might automatically think
> with the repetition of the word "case", then it is unclear to me to
> what block does (E) refer.
Not so. If it did, it should go inside the parentheses.
> If, on the other hand, (D) refers to (B)+(C) then (E) probably
> refers to (C) and then I suggest to make this more clear by replacing
> "in the case of replication weights" in (E) by "in the case of
> summarized data".
>
That would be wrong. Data can be summarized by means of groups (and SDs, which are unused, hence the suboptimality), _including_ the case where all elements are identical.
> I suggest to change "even wrong" in (E) into the more downtoearth "wrong".
That would seem to be a matter of taste.
Howver, "equivalently" in (B) does not look right.
>
> (For the record: I prefer something like my original explanation of
> the problem with (C), instead of (D)+(E)+(F):
> "With summarized data the standard errors get smaller with
> increasing numbers of observations w_i. However, when for instance all
> w_i are multiplied with the same constant larger than one, the
> reported standard errors do not get smaller since the w_i are defined
> apart from an arbitrary positive multiplicative constant. Hence the
> reported standard errors tend to be too large and the reported t
> values and the reported number of significance stars too small.
> Obviously, also the reported number of observations and the reported
> number of degrees of freedom are too small."
> Note that with heteroskedasticity, _the_ residual standard error
> has no meaning.)
>
> Finally, about the original text: (B) and (C) mention only y_i, not
> x_i, while this is about entire observations. Maybe this can remedied
> also?
>
> Arie
>
> On Tue, Nov 28, 2017 at 1:01 PM, peter dalgaard < [hidden email]> wrote:
>> My local Rdevel 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 positive integers
>> w_i, that each response y_i is the mean of w_i unitweight
>> observations (including the case that there are w_i observations
>> equal to y_i and the data have been summarized). However, in the
>> latter case, notice that withingroup variation is not used.
>> Therefore, the sigma estimate and residual degrees of freedom may
>> be suboptimal; in the case of replication weights, even wrong.
>> Hence, standard errors and analysis of variance tables should be
>> treated with care.
>>
>> OK?
>>
>>
>> pd
>>
>>
>>> On 12 Oct 2017, at 13:48 , Arie ten Cate < [hidden email]> wrote:
>>>
>>> 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 and the p value.
>>> Also the number of degrees of freedom is not correct. (The parameter
>>> values are correct.)"
>>>
>>> A remark about the glm example: the Reference manual says: "For a
>>> binomial GLM prior weights are used to give the number of trials when
>>> the response is the proportion of successes ....". Hence in the
>>> binomial case the weights are frequencies.
>>> With y < 0.51 and w < 100 you get the same result.
>>>
>>> Arie
>>>
>>> On Mon, Oct 9, 2017 at 5:22 PM, peter dalgaard < [hidden email]> wrote:
>>>> 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 < [hidden email]> 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)
>>>>> < [hidden email]> 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>>> Sent: Sunday, 08 October, 2017 14:55
>>>>>> To: [hidden email]
>>>>>> 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, [hidden email] 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: Rdevel [mailto: [hidden email]] On Behalf Of Arie ten Cate
>>>>>> Sent: Saturday, 07 October, 2017 9:36
>>>>>> To: [hidden email]
>>>>>> 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],n1)) # restore the original data
>>>>>> yb < c(y,rep(y[4],n1))
>>>>>> 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
>>>>>>
>>>>>> ______________________________________________
>>>>>> [hidden email] mailing list
>>>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>>>
>>>>> ______________________________________________
>>>>> [hidden email] mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/rdevel>>>>
>>>> 
>>>> Peter Dalgaard, Professor,
>>>> Center for Statistics, Copenhagen Business School
>>>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>>>> Phone: (+45)38153501
>>>> Office: A 4.23
>>>> Email: [hidden email] Priv: [hidden email]
>>>>
>>
>> 
>> Peter Dalgaard, Professor,
>> Center for Statistics, Copenhagen Business School
>> Solbjerg Plads 3, 2000 Frederiksberg, Denmark
>> Phone: (+45)38153501
>> Office: A 4.23
>> Email: [hidden email] Priv: [hidden email]

Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: [hidden email] Priv: [hidden email]
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