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st: RE: RE: RE: Regression across variables
Don't be misled; I am not a statistician myself
and indeed have no formal training in it worth
However, whatever is posted on Statalist is open
to challenge by anyone who can expose error and/or
put forward a better solution, irrespective of
As I understand it, your molarity values are not
variables at all, but constants which
act as gold standards or targets for your variables.
Whether it makes sense to combine the analyses is difficult
to say without understanding the experimental set-up.
There is much advantage in a unified analysis, especially
if in some sense the errors behave similarly across
molarities, but deciding that might be helped by an
initial exploratory analysis, such as
. dotplot A B C D
Things might look simpler on a log scale.
> Thanks Nick - any implication of non-orthodoxy is purely my
> ignorance in
> these matters. My formal stat background is pretty weak.
> What I was trying
> to show is that there is in effect a variable orthogonal to
> the matrix of
> observations (the Molarity value) that I would like to
> regress the row of
> values for each observation against the row of Molarity
> values (rather than
> the column of A values against the column of B values, for example).
> The question would be how to introduce the molarity values
> into the dataset
> (each variable corresponds to a concentration level that is
> being tested)
> and how to tell stata to use it in the regression.
> If the answer is the same, I'll just have to plug away and
> see if I can
> figure out how my mental picture fits into what you said.
> I appreciate the help!
> As I understand it, this is more orthodox
> than you imply, and you could think
> of the analysis as a series of regressions, except that
> you have no covariates, at least that you're
> showing us. That's not fatal, however.
> . regress A
> says in effect estimate the mean of A,
> and much of the output you get is based
> on the assumption that A follows, or
> should follow, a normal (Gaussian, central)
> Following that with
> . test _cons = 0.5
> is, perhaps, a long-winded way of going
> . ttest A = 0.5
> except that if you do have covariates,
> the -regress- framework is the one on
> which you can build. Ronan Conroy's
> paper in SJ 2(3) 2002 is a very nice
> example of this principle.
> Having said that, the assumption of normality
> is important. It wouldn't surprise me if the
> distributions were skewed and (say) gamma-like,
> so that -glm- is then a better framework.
> > Hi Statalisters. I'm trying to get Stata to perform a
> > regression in a data
> > structure different from the usual yvar xvar arrangement.
> > I'll diagram the
> > data set to show what I mean:
> > Molarity 0.5 1 2 3
> > Variable A B C D
> > Observ1 .22 .45 .99 1.4
> > Observ2 .23 .5 .98 1.5
> > Observ3 .19 .38 1.1 1.42
> > Molarity in this case would be the constant associated with
> > each variable.
> > The observations are measurements of the system attempting
> > to quantify the
> > molarity. The idea would be to generate additional
> > variables that contain
> > the various regression results of the observations vs Molarity.
> > My data set at this point is just variable name against
> > observation number.
> > I don't know how to associate each variable with the
> > corresponding molarity,
> > or how to tell Stata to perform a regression in this way.
> > Do I have to
> > -reshape- or is there another way?
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