Gisella Young <[email protected]>:
It may be that you are looking for a simple stacked bar graph over
income quintiles or deciles or the like, as opposed to a parametric
smooth over income quantiles. If so, you might want to adapt one of
this pair of example graphs to your needs:
clear all
sysuse nlsw88
ren industry i
tab i, g(ind)
g w=round(uniform()*20)
la var w "fake survey weight"
_pctile wage [pw=w], nq(5)
g q=1 if wage<=r(r1)
forv i=2/5 {
replace q=`i' if wage>r(r`=`i'-1') & wage<=r(r`i')
}
loc y
forv i=1/12 {
loc l "`=substr("`: var la ind`i''",4,.)'"
loc y `"`y' lab(`i' "`l'")"'
loc lv`i' `"la var ind`i' "`l'" "'
}
gr bar ind* [pw=w], stack over(q) name(b) leg(`y')
collapse ind* [pw=w], by(q)
forv i=2/12 {
replace ind`i'=ind`i'+ind`=`i'-1'
}
loc v
forv i=1/12 {
`lv`i''
loc v "ind`i' `v'"
}
tw bar `v' q, name(tw)
Note that the commands above destroy the data in memory, so make sure
you -preserve- or -save- first as appropriate. Also note that there
is no guarantee that the distributions of income by occupation, or
occupation by income category, display any sort of stochastic
dominance that would allow easy ranking of occupations.
See also http://www.stata.com/capabilities/graphexamples.html
On Sun, Nov 30, 2008 at 10:37 AM, Maarten buis <[email protected]> wrote:
> --- Gisella Young <[email protected]> wrote:
>> On Maarten Buis's suggestion, I am not sure why I would really need
>> a regression - I get from his email that this is basically for
>> smoothing?
>
> Yes, as income in the example dataset (and I assume in your dataset as
> well) is a continuous variable, there just aren't enough cases for each
> income value to estimate the proportions.
>
>> Since I actually want to plot the actual data (but realise
>> that this needs smoothing),
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