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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 09:38:11 +1100 |

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. <wendy.garrard@vanderbilt.edu> 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: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ramani > Gunatilaka > Sent: Wednesday, November 02, 2005 2:50 PM > To: statalist@hsphsun2.harvard.edu > 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 <paul.millar@shaw.ca> 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: Richard.A.Williams.5@ND.Edu > > >WWW (personal): http://www.nd.edu/~rwilliam > > >WWW (department): http://www.nd.edu/~soc > > >* > > >* For searches and help try: > > >* http://www.stata.com/support/faqs/res/findit.html > > >* http://www.stata.com/support/statalist/faq > > >* http://www.ats.ucla.edu/stat/stata/ > > > > * > > * For searches and help try: > > * http://www.stata.com/support/faqs/res/findit.html > > * http://www.stata.com/support/statalist/faq > > * http://www.ats.ucla.edu/stat/stata/ > > > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**RE: st: Regressing with variables with missing values***From:*"Garrard, Wendy M." <wendy.garrard@Vanderbilt.Edu>

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