[R] Logistic regression with multiple imputation
jeremy.miles at gmail.com
Wed Jun 30 07:31:02 CEST 2010
First, newer versions of SPSS have dramatically improved their ability
to do stuff with missing data - I believe it's an additional module,
and in SPSS-world, each additional module = $$$.
Analyzing missing data is a 3 step process. First, you impute,
creating multiple datasets, then you analyze each dataset in the
conventional way, then you combine the results. There are two (that
I know of) packages for imputaton - these are mi and mice. rseek.org
will find them for you.
Hope that helps,
On 29 June 2010 22:14, Daniel Chen <news at pushih.com> wrote:
> I am a long time SPSS user but new to R, so please bear with me if my
> questions seem to be too basic for you guys.
> I am trying to figure out how to analyze survey data using logistic
> regression with multiple imputation.
> I have a survey data of about 200,000 cases and I am trying to predict the
> odds ratio of a dependent variable using 6 categorical independent variables
> (dummy-coded). Approximatively 10% of the cases (~20,000) have missing data
> in one or more of the independent variables. The percentage of missing
> ranges from 0.01% to 10% for the independent variables.
> My current thinking is to conduct a logistic regression with multiple
> imputation, but I don't know how to do it in R. I searched the web but
> couldn't find instructions or examples on how to do this. Since SPSS is
> hopeless with missing data, I have to learn to do this in R. I am new to R,
> so I would really appreciate if someone can show me some examples or tell me
> where to find resources.
> Thank you!
> [[alternative HTML version deleted]]
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
More information about the R-help