Hello everyone,
I encountered a problem trying to introduce observation-level priors. I expected that the imputation for the specifed observations would be better (closer to my expectations, smaller variation). What indeed happens is three things:
1) The convergence process completely changes. The number of iterations goes down by at least 50% compared to not using the prior. I tried different priors specifiying information from 3 to 50 observations; the same thing always occurred. The imputation without the priors works fine and diagnostics indicate a good fit of the imputation model.
2) The imputations for a lot of other variables and observations not specified by the prior changed tremendously. Imputation diagnostics look a lot worse for many variables even if I specify only three variables.
3) Imputations for the observations for which priors are introduced change only slightly.
My code:
pr3 <- matrix(c(75, 76, 77, 25, 25, 25, 1, 1, 1, 2.29, 2.29, 2.28, 0.95, 0.95, 0.95), nrow=3, ncol=5)
pr3
a.out.new3 <- amelia(macimp, m=5, ts = "_j", cs = "countries", polytime=0, intercs=TRUE, p2s=2, empri=0.01*nrow(macimp), priors = pr3)
My data encompass 1127 observations and 133 variables (of which 48 are year dummies).
A non-related problem occurs when I use the imputation without priors and try to create the overdispersion plot with 5 imputations.
I get an error message:
A vector of this size cannot be allocated [translated from German].
When I use only 3 imputations, I get an overdispersion graph that looks fine.
Thank you for any suggestions how to solve these problems!
Nadine
--
Nadine Reibling
PhD candidate
Mannheim Centre for European Social Research
A5,6
68131 Mannheim
0049-621-181-3302
nreiblin@mail.uni-mannheim.de
Website: http://cdss.uni-mannheim.de/index.php?id=346