Hi all,
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?
Thanks so much,
Ramani
On 03/11/05, Garrard, Wendy M. <[email protected]> wrote:
> Ramani,
> The MAR assumption is pretty robust to some violations. The main issue
> for MAR is whether you have some observed covariates that provide
> information about the missing values. For example, if household income
> is missing, then other variables, if observed, may provide some basis
> for (e.g., zip code, occupation, education level) plausible estimation.
>
> If you have some good covariates you may be able to construct a
> relatively simple regression model to come up with some plausible
> estimates of the missing values. Note -- if you have good covariates
> multiple imputation is also an option. If you don't have observed
> covariate information, and the missing data is non-random (MNAR), then
> more specialized (and probably complex) models are required for handling
> the missing data.
>
> If you can justify MAR, the -impute- command may help you, although the
> multiple imputation algorithms are more cutting edge these days.
>
> Cheers,
> wg
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Ramani
> Gunatilaka
> Sent: Wednesday, November 02, 2005 2:50 PM
> To: [email protected]
> Subject: Re: st: Regressing with variables with missing values
>
> Thanks, Paul. I did download listmiss and use it. Now my dilemma is that
> the main culprits appear non-random wrt the dependent variable according
> to listmiss (ie. t and p values appear in yellow with stars). That means
> that I can't use ice because that assumes that the missing observations
> are missing at random. I'd be grateful for any suggestions as to what I
> should do next.
> Ramani
>
> On 03/11/05, Paul Millar <[email protected]> wrote:
> > You might also use the post-estimation command - listmiss - to find
> > which variables are the main culprits and which ones have missing
> > values that are non-random wrt the dependent variable.
> > ssc install listmiss
> >
> > - Paul Millar
> >
> > At 09:18 AM 02/11/2005, you wrote:
> > >At 10:52 AM 11/2/2005, Ramani Gunatilaka wrote:
> > >>Dear Statalist,
> > >>This may seem a stupid question for the statisticians among you but
> > >>I'd appreciate some help.
> > >>I want to run a regression on cross-section data with lots of
> > >>variables, some of which have missing values. When I do that, Stata
> > >>estimates the model using only the observations which have values
> > >>for all variables. I downloaded tabmiss and rmiss2 as in the relvant
>
> > >>FAQ and the commands would certainly help in enabling me to decide
> > >>which variables to drop. But is there any way that I could retain
> > >>all the variables with their missing values and make allowance for
> > >>the missing values by including a dummy for missing variables?
> > >
> > >The way you retain the missing values is by recoding them to a
> > >non-missing value, e.g. the variable's mean. This has all sorts of
> > >problems though. The MD dummy variable indicator that you propose
> > >used to be popular but has since been discredited. See Paul
> > >Allison's Sage book "Missing Data."
> > >
> > >For a synopsis of basic strategies and their pros and cons, see
> > >
> > >http://www.nd.edu/~rwilliam/stats2/l12.pdf
> > >
> > >That handout is weak in discussing more advanced methods, although it
>
> > >does allude to them. You might check out Royston's -ice- package,
> > >which was recently updated and discussed in the Stata Journal. Use
> > >
> > >-findit ice-
> > >
> > >
> > >-------------------------------------------
> > >Richard Williams, Notre Dame Dept of Sociology
> > >OFFICE: (574)631-6668, (574)631-6463
> > >FAX: (574)288-4373
> > >HOME: (574)289-5227
> > >EMAIL: [email protected]
> > >WWW (personal): http://www.nd.edu/~rwilliam
> > >WWW (department): http://www.nd.edu/~soc
> > >*
> > >* For searches and help try:
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> >
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> >
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