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Re: st: error bars


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: error bars
Date   Wed, 18 Jun 2008 21:21:39 +0100 (BST)

Have a look at:
R. Newson (2003), "Confidence intervals and p-values for delivery to
the end user", The Stata Journal, 3(3), pp. 245--269.
http://www.stata-journal.com/article.html?article=st0043

I actually refered to that earlier (see below)

-- Maarten


--- moleps islon <moleps2@gmail.com> wrote:

> I' making a double error bar graph- e.g two different variables x and
> y with error bars categorised according to z. I've tried using ciplot
> x y, by(z) however I get a graph with two identical error bars
> (though
> different symbol, but identical values) for each instance of z. Any
> idea how to remedy this? If I need to make bars (a bar graph)  for
> each category and then have the positive part of the error bar
> projecting from the top- how do I go about this?
> 
> Regards
> 
> 
> On Mon, Jun 16, 2008 at 2:23 PM, Nick Cox <n.j.cox@durham.ac.uk>
> wrote:
> > Standard errors can come from lots of places, including the -ci-
> > command.
> >
> > Moleps seems to be implying that -ci- does the right calculation
> for his
> > or her purposes.
> >
> > (I note that Moleps has yearly results, but -ci- for separate years
> does
> > nothing about any time series structure in the data, for example
> about
> > taking serial correlation into account.)
> >
> > -ciplot- and -stripplot- from SSC both do -ci-type calculations and
> > graphing in one.
> >
> > Nick
> > n.j.cox@durham.ac.uk
> >
> > Maarten buis
> >
> > --- moleps islon <moleps2@gmail.com> wrote:
> >> I need to make a an error-bar graph categorised by year. I've
> tried
> >> using serrbar mean(x) etc, but I cant find a command for the
> standard
> >> error. Do I need to run a CI, generate a new variable from the ci
> >> result and feed that into serrbar?
> >
> > There are many ways of doing this. For instance you can use methods
> > discussed in (Buis 2007) (a convenient estimatation command would
> in
> > this case be -mean-), or you can remember that the standard error
> of
> > the mean is the standard deviation divided by the square root of
> the
> > number of observations, like in the example below:
> >
> > *-------------- begin example -----------------------
> > sysuse nlsw88, clear
> > gen mis = missing(wage, age)
> > bys age: egen mwage = mean(wage)
> > bys age: egen sdwage = sd(wage)
> > bys age mis: gen se = sdwage/sqrt(_N) if mis == 0
> > serrbar mwage se age, scale(1.96)
> > *-------------- end example -------------------------
> >
> > Notice that a fixed scale is slightly problematic here as idealy
> this
> > scale should depend on the number of observations (a t-test), but
> 1.96
> > should work fine in large samples. For a more flexible approach,
> where
> > you can take all this into account see: (Newson 2003)
> >
> > -- maarten
> >
> > M.L. Buis (2007), "Stata tip 54: Where did my p-values go?", The
> Stata
> > Journal, 7(4), pp.584--586.
> > http://home.fsw.vu.nl/m.buis/wp/pvalue.html
> >
> > R. Newson (2003), "Confidence intervals and p-values for delivery
> to
> > the end user", The Stata Journal, 3(3), pp. 245--269.
> > http://www.stata-journal.com/article.html?article=st0043
> >
> >
> > *
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> >
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> 


-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


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