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02-03-2015 08:36 AM

DO I have Denim regression in SAS? I saw POC CALIS but I don't know how to use it.

Below some data for which I would like to performs deming regression.

**data **example;

do id=**1** to**50**;

x=**10** + NORMAL(**0**);

y=**20** + NORMAL(**1**);

output;

end;

run;

Thanks in advantage,

Barbara Pomili - Biostatistician specialist at Novartis vaccines

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02-03-2015 09:53 AM

Do you mean "Deming Regression"?

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02-04-2015 01:12 PM

Yes sorry I made I big mistake.

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02-04-2015 01:13 PM

I meant DEMING regression

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02-04-2015 02:56 PM

Google comes up with the answer quickly

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02-05-2015 02:54 AM

I know this macro. I need to know if there is a PROC that perform direcly Deming regression and if yes how to use it.

Thanks,

BArbara

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02-05-2015 12:22 PM

Well, again, searching the SAS documentation doesn't turn up anything for Deming Regression, and searching that Internet thing doesn't turn up anything other than that macro, so ...

I point out that a very special case of Deming Regression, called Orthogonal Regression, is available in SAS.

Why is that macro not sufficient?

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02-06-2015 03:41 AM

Did you see the sixth row of this Paper ?

Though **PROC NLP** and **CALIS** in SAS® can accommodate Deming regression models,

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02-06-2015 08:14 AM

I guess you already looked at proc calis here:

You have to learn one of the languages of proc calis.

I am not able to analyze your generated data, because I don’t know what model (equations) to use. It depends on your model of the measurement system.

Obviously in your generated data there is no correlation between x and y. You can “prove” it using proc calis, but in this simple case proc corr is suitable.

Gergely

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02-06-2015 09:25 AM

I have looked into A SAS® Macro for Deming Regression. One way to analyze the example dataset generated there:

data example;

do myid=1 to 200;

value=UNIFORM(0)*10;

x=value + NORMAL(0);

y=value + NORMAL(1);

output;

end;

run;

proc calis data=example meanstr;

lineqs

x = 0 * Intercept + 1. * Fx + e1,

y = mu * Intercept + beta * Fx + e2;

variance e1=1, e2;

mean Fx;

run;

The result show, that variance of the new measurements ~equal to the old.

Also the slopes of two measurements (old and new) are ~equal (=1).

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02-07-2015 02:51 AM

Thanks a lot this was really helpfoul.

Barbara