On Jun 30, 2010, at 1:14 AM, Daniel Chen wrote:
Hi,
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
In addition to the tips above, you may want to chek out:
http://www.stat.columbia.edu/~gelman/arm/missing.pdf
2010/6/30 Chuck Cleland
> On 6/30/2010 1:14 AM, Daniel Chen wrote:
> > Hi,
> >
> > I am a long time SPSS user but new to R, so please bear with me if my
> > questions seem to be too basi
There are titanic datasets in R binary format at
http://biostat.mc.vanderbilt.edu/DataSets
Note that the aregImpute function in the Hmisc package streamlines many
of the steps, in conjunction with the fit.mult.impute function.
Frank
On 06/30/2010 05:02 AM, Chuck Cleland wrote:
On 6/30/2010
On 6/30/2010 1:14 AM, Daniel Chen wrote:
> Hi,
>
> 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 surv
mitools is useful too, and I can vouch for mice. mice is easy to use,
and easy to write new imputation methods too. So it is also very flexible.
Simon.
On 30/06/10 15:31, Jeremy Miles wrote:
Hi Daniel
First, newer versions of SPSS have dramatically improved their ability
to do stuff with miss
Hi Daniel
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 y
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