Good afternoon,
I am student at the Rochester Institute of Technology and I am currently
working with a time-series data set that I am trying to impute, however I
get the following error:
Loading required package: Rcpp
##
## Amelia II: Multiple Imputation
## (Version 1.7.3, built: 2014-11-14)
## Copyright (C) 2005-2015 James Honaker, Gary King and Matthew Blackwell
## Refer to http://gking.harvard.edu/amelia/ for more information
##
amelia starting
beginning prep functions
Variables used: Volume Speed Delay Stops
Error in colSums(sapply(priors[, 1, drop = FALSE], ">", blanks)) :
'x' must be an array of at least two dimensions
Calls: amelia ... amelia.default -> amelia.prep -> amsubset -> colSums
I am calling Amelia with the following command:
amelia(dfMat, m=5, ts=5, priors=priorsMat, bouds=boundsMat, p2s=2)
where dfMat is the data matrix of size 60000x5, where the fifth column is
the time variable, priorsMat is the matrix of observational priors with 4
columns(I specify the mean and deviation for each data point). I've checked
that dfMat and priorsMat have the correct dimensions and thus I am not sure
what could be giving me this error.
I would be very grateful if somebody could give me any pointers as to what
might be the issue.
Thank you very much.
Sincerely,
Michal Kucer
--
Michal Kucer
Rochester Institute of Technology
Amelia users,
My data set consists of several environmental predictor variables, each of which are time series with seasonal and trend components. These are monthly values taken over 15 years. I have been experimenting with Amelia and have read the referenced papers and documentation, but two questions remain:
1) What is the best way to indicate the "ts" component? If this is monthly, should I assign sequential numbers to my "Month" column (1-180) indicating 12 months X 15 years and leave "Year" out of the analysis, or should I simply number my months 1-12 for each year and again only select "Month" for ts? If there is both a seasonal and a trend component to the data, will it be captured using either of these methods?
2) All of my variables (both independent and dependent) are time series, and all have missing values. If I do nothing to the data, except for maybe simple transformations to a few variables to better approach normality, are there any major precautions to imputing ts data from all ts data? Of course, I will experiment with polytime, splinetime, lags, etc.
Thank you for your help.
Regards,
Erin Graham
Hello,
The Amelia output file indicates the imputation is successful. But when I
opened up the stacked.dta file, I get this error message "dta file corrupt
The file unexpectedly ended before it should have"
Any ideas?
Thanks
Evelyn
Hi there,
In some rare occasions, Amelia is unable to converge and keeps iterating
without ever reaching the tolerance value.
I wouldn't not want to lower the tolerance since in most situations Amelia
converges to a good imputation. I'm using Amelia within a larger automated
pipeline that exhaustively tests different models, so the non-converging
situations, although rare, happen from time to time and require manual
intervention to stop the Amelia instance, restart the pipeline, etc.
Is is possible to set a maximum number of iterations so that Amelia can
quit the EM loop before reaching the desired tolerance? I revised the
documentation, but didn't find such an argument.
Thanks!
Andres