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: R-devel [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],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 ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-devel |
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:[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: R-devel [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],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 ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-devel |
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: R-devel [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: R-devel [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],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 > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-devel |
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: R-devel [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: R-devel [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],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 >> >> ______________________________________________ >> [hidden email] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel > > ______________________________________________ > [hidden email] mailing list > https://stat.ethz.ch/mailman/listinfo/r-devel -- 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/r-devel |
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: R-devel [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: R-devel [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],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 >>> >>> ______________________________________________ >>> [hidden email] mailing list >>> https://stat.ethz.ch/mailman/listinfo/r-devel >> >> ______________________________________________ >> [hidden email] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-devel > > -- > 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/r-devel |
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