Hi,
There are 535 sites in Pakistan where wild birds are counted by volunteers once per year usually in the mid of winter. I have data from 1987-2007. The data contain missing values that I want to impute through Amelia II.
I have restructured subset of data into three columns (site code, year, count). Would you please guide me what should be the model specifications (options) [knots, priors, and numeric options if required]? I get error message when I set "count" as time series variable and try to impute it.
I can provide you data if required.
Regards
Tariq Abbas
Free university of Berlin
Germany
Dear all,
I'm currently using amelia to impute income in cross-sectional data
(about 30% are missing). The distribution of the available income data
is right skewed, so I transformed the income using the natural
logarithm. Unfortunately the income data is also top-coded or
right-censored - that is all respondents with an income above a certain
limit are summarized into one category. (see example in the following
paper, dealing exactly with this problem:
http://www.fcsm.gov/07papers/Buettner.X-C.pdf)
Comparison of observed and imputed data
(I've also added the comparison of imputed and observed data in a
jpeg-file where the imputation worked and I hope that the graph is visible)
So I have two problems:
1.) In some of the data sets, when there are a lot of people in the top
category the imputation is not succesfull at all.
2.) But even if the imputation is working, the imputed values in the
upper categories are smaller than the observed values - and only very
few imputed values are above the maximum of the observed values.
Is there any way to deal with these problems in Amelia? Or do I have to
transform the income data to get a good imputation?
Thanks for your help,
Elias
--
Elias Naumann
SFB 884
Universität Mannheim
Dear All,
I am using Amelia to fill in some gaps in national accounts data (and
similar panels of data); as a result of the structure of my panel, there
is no 'cs' parameter -- just 'ts'.
I intend to use the EM algorithim to complete my panel, and then to
extract factors via PCA -- as a reuslt i have two related questions.
1/ I would like to use the tscsPlot command (or similar) to plot the
observed values and the imputations (mean + 95% confidence bands) -- is
this possible?
2/ What's the best way to use the output from the imputations to
generate the factors in the PCA? I have considered two methods, but am
unsure which is best (most valid) --
1/ fill the gaps in the panel with the the mean of the imputations and
use the single data set to extract the eigenvales and eigenvectors of
the assocaited covariance matrix - and then use these weights and the
'mean-filled in' data set to generate the factors.
2/ stack the imputation panels and use the stacked panel to generate the
eigenvalues and eigenvectors of the associated covariance matrix - and
then use the mean values from the imputation runs to fill the gaps in my
original panel, and the weights from the stacked panel?
thanks and best regards
Matt Johnson
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