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Re: st: Regressing with variables with missing values


From   Ramani Gunatilaka <ramani.gunatilaka@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Regressing with variables with missing values
Date   Mon, 7 Nov 2005 10:34:00 +1100

Nick,
Thanks, that worked. I should say, though, that I have several
variables with missing values and I tried ice with all of them and it
didn't work, so then I tried with just one.
See, this is what happens:

. /*MICE to impute missing values*/
. use uphvar02, clear

. ice incdisCh incdisCity indy02 workhrs happy r_health edyrs ln_pcy02
ln_avcityy city province male di
> vorced widowed finassets hhdebt using uricevar02, eq(incdisCh: indy02 ln_pcy02 ln_avcityy city provin
> ce finassets hhdebt, incdisCity: indy02 ln_pcy02 ln_avcityy city province finassets hhdebt, happy: ln
> _pcy02 r_health male divorced widowed) genmiss(M1) id(flag1) replace

    Variable | Command     | Prediction equation
-------------+-------------+--------------------------------------------------
    incdisCh | mlogit      | indy02 ln_pcy02 ln_avcityy city province
             |             | finassets hhdebt
  incdisCity | mlogit      | indy02 ln_pcy02 ln_avcityy city province
             |             | finassets hhdebt
      indy02 | regress     | incdisCh incdisCity workhrs happy r_health edyrs
             |             | ln_pcy02 ln_avcityy city province male divorced
             |             | widowed finassets hhdebt
     workhrs | regress     | incdisCh incdisCity indy02 happy r_health edyrs
             |             | ln_pcy02 ln_avcityy city province male divorced
             |             | widowed finassets hhdebt
       happy | mlogit      | ln_pcy02 r_health male divorced widowed
    r_health |             | [No missing data in estimation sample]
       edyrs |             | [No missing data in estimation sample]
    ln_pcy02 |             | [No missing data in estimation sample]
  ln_avcityy |             | [No missing data in estimation sample]
        city |             | [No missing data in estimation sample]
    province |             | [No missing data in estimation sample]
        male |             | [No missing data in estimation sample]
    divorced |             | [No missing data in estimation sample]
     widowed |             | [No missing data in estimation sample]
   finassets |             | [No missing data in estimation sample]
      hhdebt |             | [No missing data in estimation sample]

Imputing 1..file uricevar02.dta saved

. sort city province hhid

. compress

. save uricevar02, replace
file uricevar02.dta saved

.
.
end of do-file

. count if happy==.
   65


Would you have any ideas about what's going wrong? I'd be very grateful.
Thanks so much,
Ramani

On 07/11/05, Nick Cox <n.j.cox@durham.ac.uk> wrote:
> I've not used -ice- myself: I just recommend it!
>
> However, a quick glance suggests that you
> are misunderstanding the syntax. It may be
> that what you want is something more like
>
> uvis regress happy ln_pcy02 r_health male divorced widowed,
> gen(HAPPY)
>
> Nick
> n.j.cox@durham.ac.uk
>
> Ramani Gunatilaka
>
> > I have been following up on all the useful comments I got and have
> > been working on that ice thing to replace missing values.
> > Unfortunately the programme goes through the motions but doesn't
> > replace any missing values. I am at my wit's end. The dependent
> > variable and the one that has missing values is happy (which takes the
> > values 1-5 depending on level of happiness (the data set as a whole
> > has 6805 observations), and my code runs like this.
> >
> > use uphvar02, clear
> >
> > . ice happy ln_pcy02 r_health male divorced widowed using uricevar02,
> > cmd(regress) eq(happy: ln_pcy02 r _health male divorced widowed)
> > genmiss(M1) id(flag1) replace
> >
> > This is my output:
> >
> >     Variable | Command     | Prediction equation
> > -------------+-------------+----------------------------------
> > ----------------
> >        happy | regress     | ln_pcy02 r_health male divorced widowed
> >     ln_pcy02 | regress     | [No missing data in estimation sample]
> >     r_health | regress     | [No missing data in estimation sample]
> >         male | regress     | [No missing data in estimation sample]
> >     divorced | regress     | [No missing data in estimation sample]
> >      widowed | regress     | [No missing data in estimation sample]
> >
> > Imputing
> > [Only 1 variable to be imputed, therefore no cycling needed.]
> > 1..file uricevar02.dta saved
> >
> > . sort city province hhid
> >
> > . compress
> >
> > . save uricevar02, replace
> > file uricevar02.dta saved.
> > end of do-file
> >
> > But when I check - here's what I get. Missing values still there.
> >
> > . count if happy==.
> >    65
> >
> > Does anybody have any ideas as to what might be going wrong?
>
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