Danke sehr, herr Professor ! This one escaped me (notably because it's a
trifle far from my current interests...).
Emmanuel Charpentier
Le samedi 25 avril 2009 à 08:25 -0500, Frank E Harrell Jr a écrit :
> Emmanuel Charpentier wrote:
> > Le vendredi 24 avril 2009 à 14:11 -0700, ToddPW a écrit :
> >> 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
> >>
> >>
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> All see
>
> @Article{zha09non,
> author = {Zhang, Donghui and Fan, Chunpeng and Zhang,
> Juan and Zhang, {Cun-Hui}},
> title = {Nonparametric methods for measurements below
> detection limit},
> journal = Stat in Med,
> year = 2009,
> volume = 28,
> pages = {700-715},
> annote = {lower limit of detection;left censoring;Tobit
> model;Gehan test;Peto-Peto test;log-rank test;Wilcoxon test;location
> shift model;superiority of nonparametric methods}
> }
>
>
>
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