sas >> PROC MIXED LSMEANS

by Paige Miller » Tue, 22 May 2007 22:56:09 GMT

In PROC MIXED, you can specify the E option to the LSMEANS statement
and see what are the actual contrasts used to compute the LSMEANS. A
useful feature.

Also in PROC MIXED, you can specify, for example

repeated/local=exp(x1 x2);

and estimate the dispersion effects of a model which has log-linear
variance.

Now I understand why, when you specify the REPEATED command as above,
that the LSMEANS now have a different standard errors, compared to
when you do not use the REPEATED statement above. I cannot understand
why the LSMEANS have different estimates when you use the REPEATED
statement above, compared to when you don't use that REPEATED
statement, especially since the E option on the LSMEANS statement
gives the exact same contrasts in both cases.

Can someone explain to me why this REPEATED statement changes the
LSMEANS estimates? Thanks!


sas >> PROC MIXED LSMEANS

by Paige Miller » Tue, 22 May 2007 22:57:37 GMT


In PROC MIXED, you can specify the E option to the LSMEANS statement
and see what are the actual contrasts used to compute the LSMEANS. A
useful feature.

Also in PROC MIXED, you can specify, for example

repeated/local=exp(x1 x2);

and estimate the dispersion effects of a model which has log-linear
variance.

Now I understand why, when you specify the REPEATED command as above,
that the LSMEANS now have a different standard errors, compared to
when you do not use the REPEATED statement above. I cannot understand
why the LSMEANS have different estimates when you use the REPEATED
statement above, compared to when you don't use that REPEATED
statement, especially since the E option on the LSMEANS statement
gives the exact same contrasts in both cases.

Can someone explain to me why this REPEATED statement changes the
LSMEANS estimates? Thanks!

sas >> PROC MIXED LSMEANS

by Paige Miller » Tue, 22 May 2007 22:58:19 GMT

In PROC MIXED, you can specify the E option to the LSMEANS statement
and see what are the actual contrasts used to compute the LSMEANS. A
useful feature.

Also in PROC MIXED, you can specify, for example

repeated/local=exp(x1 x2);

and estimate the dispersion effects of a model which has log-linear
variance.

Now I understand why, when you specify the REPEATED command as above,
that the LSMEANS now have a different standard errors, compared to
when you do not use the REPEATED statement above. I cannot understand
why the LSMEANS have different estimates when you use the REPEATED
statement above, compared to when you don't use that REPEATED
statement, especially since the E option on the LSMEANS statement
gives the exact same contrasts in both cases.

Can someone explain to me why this REPEATED statement changes the
LSMEANS estimates? Thanks!

sas >> PROC MIXED LSMEANS

by stringplayer_2 » Wed, 23 May 2007 01:22:10 GMT


Paige,

In a nutshell, when you specify that the error structure has local
exponential effects, then you end up fitting a weighted regression
model with weights which are not uniform for all observations.
Parameter estimates for weighted and unweighted regressions have
the same expectations but not the same point estimates in finite
samples.

Does this clarify the issue?

Dale


---------------------------------------
Dale McLerran
Fred Hutchinson Cancer Research Center
mailto: XXXX@XXXXX.COM
Ph: (206) 667-2926
Fax: (206) 667-5977
---------------------------------------



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http://answers.yahoo.com/dir/?link=list&sid=396545433

sas >> PROC MIXED LSMEANS

by Paige Miller » Wed, 23 May 2007 02:12:12 GMT

In PROC MIXED, you can specify the E option to the LSMEANS statement
and see what are the actual contrasts used to compute the LSMEANS. A
useful feature.

Also in PROC MIXED, you can specify, for example

repeated/local=exp(x1 x2);

and estimate the dispersion effects of a model which has log-linear
variance.

Now I understand why, when you specify the REPEATED command as above,
that the LSMEANS now have a different standard errors, compared to
when you do not use the REPEATED statement above. I cannot understand
why the LSMEANS have different estimates when you use the REPEATED
statement above, compared to when you don't use that REPEATED
statement, especially since the E option on the LSMEANS statement
gives the exact same contrasts in both cases.

Can someone explain to me why this REPEATED statement changes the
LSMEANS estimates? Thanks!

sas >> PROC MIXED LSMEANS

by Paige Miller » Wed, 23 May 2007 20:21:42 GMT


Dale, yes it clarifies the issue somewhat, and I certainly believe
you, as you have demonstrated "mad skillz" (as my teenagers would say)
in this area.

But I want to ask a few more details. The Mixed Model is formulated
as:

y = XB + Zu + e

and the variance of u is the matrix G and the variance of e is the
matrix R; and G and R are independent.

I was thinking that the least squares means depends only on X and B
and Z and u. The reason I bring this up is that the when you request
PROC MIXED to estimate the dispersion effects of a log-linear variance
model, you are changing the value of the matrix R. Changing the
estimate of R does not effect the estimates of B, which in turn
determine the least squares means. Where am I wrong?

sas >> PROC MIXED LSMEANS

by vontressms » Wed, 23 May 2007 20:46:55 GMT

n May 23, 7:21 am, Paige Miller < XXXX@XXXXX.COM > wrote:

Paige,

You are close to the answer. The estimable functions generated by the
E option only depend on X. There is a good explanation of them in the
help file for GLM in a description of the type III sums of squares.
You get different answers when you use the repeated statement because
the estimates of B, u, and e change since you have specified a
different covariance matrix for the multivariate normal. However, the
X matrix is not changed by the covariance matrix specification, so you
get the same estimable functions from the E option.

Mark


sas >> PROC MIXED LSMEANS

by vontressms » Wed, 23 May 2007 20:47:21 GMT

n May 23, 7:21 am, Paige Miller < XXXX@XXXXX.COM > wrote:

Paige,

You are close to the answer. The estimable functions generated by the
E option only depend on X. There is a good explanation of them in the
help file for GLM in a description of the type III sums of squares.
You get different answers when you use the repeated statement because
the estimates of B, u, and e change since you have specified a
different covariance matrix for the multivariate normal. However, the
X matrix is not changed by the covariance matrix specification, so you
get the same estimable functions from the E option.

Mark


sas >> PROC MIXED LSMEANS

by Paige Miller » Wed, 23 May 2007 21:08:45 GMT

n May 23, 8:46 am, XXXX@XXXXX.COM wrote:

DING DING DING!

That was the sound of the light bulb turning on over Paige's head. I
understand now. Thanks.

But this leads to a practical problem. Seems to me that you can choose
to receive Least Squares Means that make sense to me (since I have a
balanced data set, I expect the LS Means to equal to cell means)
without being able to estimate the dispersion effects, or I can choose
to estimate the dispersion effects and get LSMeans that don't seem
intuitive and would be hard to explain.

Is that the way you see it?


sas >> PROC MIXED LSMEANS

by Paige Miller » Wed, 23 May 2007 21:08:58 GMT

n May 23, 8:46 am, XXXX@XXXXX.COM wrote:

DING DING DING!

That was the sound of the light bulb turning on over Paige's head. I
understand now. Thanks.

But this leads to a practical problem. Seems to me that you can choose
to receive Least Squares Means that make sense to me (since I have a
balanced data set, I expect the LS Means to equal to cell means)
without being able to estimate the dispersion effects, or I can choose
to estimate the dispersion effects and get LSMeans that don't seem
intuitive and would be hard to explain.

Is that the way you see it?


sas >> PROC MIXED LSMEANS

by steven.c.denham » Wed, 23 May 2007 23:52:23 GMT

n Wed, 23 May 2007 06:08:45 -0700, Paige Miller < XXXX@XXXXX.COM >
wrote:

statement
A
above,
understand

/dragging out soapbox sound
/climbs onto soapbox

There are some days when I wish the term Least Squares Mean wasn't so
ingrained into our thinking. This would be one of them. We're a fair bit
away from the OLS solution, so these particular best linear unbiased
estimates aren't "least squares" at all. When considering the dispersion
effects, they aren't even means. They are BLU estimates of central
tendency, that we happen to call LSMeans. I have to remind myself of this
continually. They aren't really means.

/notices that noose is firmly attached around neck
/hopes no one kicks soapbox back into corner

Steve Denham
Mathematical Biologist
Monsanto Co.

sas >> PROC MIXED LSMEANS

by Paige Miller » Thu, 24 May 2007 00:33:55 GMT

On May 23, 11:52 am, XXXX@XXXXX.COM (Steve Denham)



This is a much better way of saying what I was thinking when I made my
last post, and directly addresses my concern about using the LSMEANS
while estimating dispersion effects. Thank you.


I shall come to your virtual defense if necessary.

sas >> PROC MIXED LSMEANS

by stringplayer_2 » Thu, 24 May 2007 02:43:30 GMT

-- Steve Denham < XXXX@XXXXX.COM > wrote:


I have to disagree here. Weighted least squares estimates are
every bit an OLS solution. The weighted ls estimation problem can
be written as

bhat = inv(X'WX)*(X'WY)

Let U=X'sqrt(W),
Z=sqrt(W)Y

Then

bhat = inv(U'U)*(U'Z)

which is the usual OLS estimation problem.


I have to disagree again. Weighted means are every bit as much
means as unweighted means. If the weights are inversely proportional
to the variance of the response, then we have optimal estimates
of the true mean.


Yes, this is a point of agreement. But just what are these BLU
estimates of central tendency? How do we interpret them?

Unless otherwise constructed, the LSMeans are estimates of what
the mean would be for each level of categorical predictor variable
A given that all other categorical variables have a completely
balanced distribution and that all continuous variables are
observed at their mean values. To the extent that there is a
population in which this assumption can be expected to hold true,
then the LSMeans are estimates of the expectation across the
various levels of A in that population.

Sometimes it is not at all reasonable to believe that there is
such a population. The LSMeans can be computed under other
distributions of the categorical variables if you specify the
OM option. When you specify the OM option, the LSMeans will be
much more like the observed means when the data are not balanced.

If your design matrix has no continuous variable and the categorical
variables are balanced at all levels of A, then the LSMeans will
be identical to raw means. However, if there are continuous
variables in the data set (as Paige must have since he is using
the local=exp(X1 X2) option), then LSMeans will almost never be
the same as the raw mean, even if all categorical predictor
variables are balanced.

Dale


---------------------------------------
Dale McLerran
Fred Hutchinson Cancer Research Center
mailto: XXXX@XXXXX.COM
Ph: (206) 667-2926
Fax: (206) 667-5977
---------------------------------------



____________________________________________________________________________________
Moody friends. Drama queens. Your life? Nope! - their life, your story. Play Sims Stories at Yahoo! Games.
http://sims.yahoo.com/

sas >> PROC MIXED LSMEANS

by Paige Miller » Fri, 25 May 2007 01:27:33 GMT


First, I don't have continuous predictors, all of my X variables are
categorical, and if I used the notation X1 X2 and it confused you, my
apologies.

More importantly, I am looking for some guidance. I do want estimates
of the LSMeans in each cell. I also want to estimate a log-linear
variance model, but if the cost of doing so is to get LSMeans that I
cannot explain and don't look right to scientists who can
independently compute the mean of their data in each cell, then maybe
I don't want the log-linear variance. Can I get reasonable looking
LSMeans and log-linear variance? It appears not. So as I see things
now, my choice is simply to pick one or the other (reasonable looking
LSMeans or log-linear variance) and live with that.

Is that how you see things? Do I have other choices? Does it make
sense to compute the LSMeans in one invocation of PROC MIXED, and the
variance estimates in a second invocation of PROC MIXED?

Thanks for any advice you might have.

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Intro Price              Non-est           5
Offer                    Non-est           6
Premium Offers           Non-est           7
Premium Services         Non-est           8
Price Choice             Non-est           9
Registration             Non-est          10

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