R help - Apr 2009 - Multiple Imputation in mice/norm

I'm trying to use either mice or norm to perform multiple imputation to fill
in some missing values in my data.  The data has some missing values because
of a chemical detection limit (so they are left censored).  I'd like to use
MI because I have several variables that are highly correlated.  In SAS's
proc MI, there is an option with which you can limit the imputed values that
are returned to some range of specified values.  Is there a way to limit the
values in mice?  If not, is there another MI tool in R that will allow me to
specify a range of acceptable values for my imputed data?

Thanks for the help,
Todd


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Le vendredi 24 avril 2009 ? 14:11 -0700, ToddPW a ?crit
:
> I'm trying to use either mice or norm to perform multiple imputation to
fill
> in some missing values in my data.  The data has some missing values
because
> of a chemical detection limit (so they are left censored).  I'd like to
use
> MI because I have several variables that are highly correlated.  In
SAS's
> proc MI, there is an option with which you can limit the imputed values
that
> are returned to some range of specified values.  Is there a way to limit
the
> values in mice?  
You may do that by writing your own imputation function and assign them
for the imputation of particular variable (see argument
"imputationMethod" and details in the man page for "mice").
>                  If not, is there another MI tool in R that will allow me
to
> specify a range of acceptable values for my imputed data?
In the function amelia (package "Amelia"), you might specify a
"bounds"
argument, which allows for such a limitation. However, be aware that
this might destroy the basic assumption of Amelia, which is that your
data are multivariate normal. Maybe a change of variable is in order (e.
g. log(concentration) has usually much better statistical properties
than concentration).

Frank Harrell's aregImpute (package Hmisc) has the "curtail"
argument
(TRUE by default) which limits imputations to the range of observed
values.

But if your left-censored variables are your dependent variables (not
covariates), may I suggest to analyze these data as censored data, as
allowed by Terry Therneau's "coxph" function (package
"survival") ? code
your "missing" data as such a variable (use :
coxph(Surv(min(x,<yourlimit>,na.rm=TRUE),
           !is.na(x),type="left")~<Yourmodel>) to do this
on-the-fly).

Another possible idea is to split your (supposedly x) variable in two :
observed (logical), and value (observed value if observed, <detection
limit> if not) and include these two data in your model. You probably
will run into numerical difficulties due to the (built-in total
separation...).

HTH,

					Emmanuel Charpentier
> Thanks for the help,
> Todd
> 
>