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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
Maria Fleischmann <maria.s.fleischmann@gmail.com> |

Subject |
Re: st: mi impute: ologit, mlogit, logit in one equation? |

Date |
Tue, 17 May 2011 10:50:01 -0400 |

Maria, as far as I know, the only way to do this is to do a separate _mi impute- for each variable to be imputed and then use -mi add- (or -mi append- depending on your specific situation) Rich On 5/17/11 10:42 AM, Maria Fleischmann wrote: > Thanks, Richard, for this suggestion. I will consider it! > > But just out of interest: is it possible to impute missing values with > a non-monotone missing structure if the variables should be imputed > with different regression equations (mlogit, logit, reg)? > Which should be something like: > mi impute (regress) var1 (ologit) var2 var3 (mlogit) var5 = var0 var4 > i.var6 var7 i.var8, replace > > On 17 May 2011 15:49, Richard Goldstein <richgold@ix.netcom.com> wrote: >> >> Maria, >> >> since you have already imputed the data, why not use -mi import- and >> then you can use -mi estimate- (or any other built-in tool)? >> >> Rich >> >> On 5/17/11 9:43 AM, Maria Fleischmann wrote: >>> Dear statalister, >>> >>> I am currently trying to impute multiple individual-level variables >>> from a clustered dataset. >>> I did so with stata command ice, but I would rather like to use mi >>> impute due to the mi estimate command. >>> My equation in ice (without taking into account the layered structure >>> of the data) was the following, where I impute variabels by ologit, >>> mlogit, logistic or linear regression, all delineated in one command: >>> >>> ice var0 var1 o.var2 o.var3 var4 m.var5 i.var6 var7 i.var8, m(5) >>> saving(20110511_ice_1, replace) >>> >>> Now I would like to transfer this to a mi impute command, but this >>> raises the question whether I can use mlogit, ologit, linear >>> regression in the same equation if the missing structure in my data is >>> not monotonic? >>> I think the mi impute command should look somehow like this: >>> >>> mi set mlong >>> mi set M=5 >>> mi register imputed var1 var2 var3 var5 >>> mi register regular var0 var4 var6 var7 var8 >>> mi impute (regress) var1 (ologit) var2 var3 (mlogit) var5 = var0 var4 >>> i.var6 var7 i.var8, replace >>> >>> This however does not work, because the data is not monotonic? >>> So I tried to do the imputation separate for all the different >>> regression types, which also does not work, because the missing >>> structure is still not monotonic and I have more variables for one >>> type of regression, e.g. ologit for var2 and var3. >>> But also when doing the imputation separate for each variable (which I >>> find highly inconvenient especially due to the large number of >>> variables I want to impute), stata reports problems because I am >>> imputing variables with variables that also have missing values. Do I >>> just have to force stata to impute or is there a more elegant way of >>> solving my problem (which I hope and think there is)? >>> These are all problems I encountered besides the fact that I also >>> would like to account for the clustering of the data. >>> >>> Thanks you for your help! >>> >>> Maria > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: mi impute: ologit, mlogit, logit in one equation?***From:*Maria Fleischmann <maria.s.fleischmann@gmail.com>

**Re: st: mi impute: ologit, mlogit, logit in one equation?***From:*Richard Goldstein <richgold@ix.netcom.com>

**Re: st: mi impute: ologit, mlogit, logit in one equation?***From:*Maria Fleischmann <maria.s.fleischmann@gmail.com>

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