Dear Dr Matthew,
Thank you so much for your answer. I'am very grateful for your help.
Wish you all the best in your career
Best regards,
Mouna
Le Vendredi 29 août 2014 15h22, Matt Blackwell <mblackwell(a)gov.harvard.edu> a écrit
:
Hi Mouna,
When you specify ords = "X5", you are saying that X5 is an ordinal variable and
so the imputation are constrained to only take values that are observed in the data. An
alternative is to use the "bounds" argument to constrain the imputations to be
within certain bounds. See Section 4.6.3 of the Amelia manual here:
http://r.iq.harvard.edu/docs/amelia/amelia.pdf
Cheers,
Matt
~~~~~~~~~~~
Matthew Blackwell
Assistant Professor of Government
Harvard University
url:
http://www.mattblackwell.org
On Fri, Aug 29, 2014 at 9:10 AM, mouna kessentini <kessentini_mouna(a)yahoo.fr>
wrote:
Dear professors,
I am a Phd student at the University of Paris VIII and I am working on financial issue in
a panel data. I've come to use the multiple imputation for my missing data and
I've been very interested in your method.
Since I was working with R, I installed your Amelia II Package which has perfectly worked
for my case. However, when I started to look closely to the values imputated I realised
that the method has replaced the missing values with negative ones which in my case was
non significant and non relevant. Beside the negative issue, I have some problem with
variable ranging between 0 and 1. Indeed, I have a variable wich representes a percentage
but when imputated, the missing values exceed the number 1 which is wierd.
Could you have the kindness to enlighten me of a possible solution to these two problems,
knowing that by adding in the command ( ords ="X5", p2s = 0), the negative value
disappear and the imputed data for X5 are numeric
a.out<- amelia(Mydata, m = 5, ts = "year", cs = "id", ords
="X5", p2s = 0).
This constraint (ords ="X5", p2s = 0) in the command is correct!! Thank you in
advance for your answer.
Best regards
Mouna kessentini
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