Usually if you transform the data before Amelia, impute, and then
transform back, you can avoid this problem. See
http://gking.harvard.edu/amelia/node22.html for more details about this
and other procedures in more complicated situations.
Best of luck,
Gary King
: Gary King, King(a)Harvard.Edu
http://GKing.Harvard.Edu :
: Center for Basic Research Direct (617) 495-2027 :
: in the Social Sciences Assistant (617) 495-9271 :
: 34 Kirkland Street, Rm. 2 HU-MIT DC (617) 495-4734 :
: Harvard U, Cambridge, MA 02138 eFax (617) 812-8581 :
On Mon, 7 Jun 2004, Leonelo Bautista wrote:
I'm imputing values for missing blood lipids
(roughly 20% out of 3000
observations) in a cross-sectional data set. Some of the imputed values are
not possible (for example, negative triglycerides levels). I'm not sure how
to handle these imputed values. Should I just ignore then and go ahead with
the analysis? Is it possible to impose restrictions on the model (using
Amelia) to make all triglyceride values positive?
Any suggestions will be appreciated.
Leonelo E. Bautista, MD, DrPH
Assistant Professor
University of Wisconsin Medical School
Population Health Sciences
610 Walnut Street, 703 WARF
Madison, WI 53726-2397
Phone: (608)265-6176
Fax: (608)263-2820