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 survey data
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
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 cclel...@optonline.net
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
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
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 of about 200,000 cases and I am trying to predict
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
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
7 matches
Mail list logo
