AmeliaView GUI Guide
2024-04-23
Below is a guide to the AmeliaView menus with references back to the
users’s guide. The same principles from the user’s guide apply to
AmeliaView. The only difference is how you interact with the program.
Whether you use the GUI or the command line versions, the same
underlying code is being called, and so you can read the command
line-oriented discussion above even if you intend to use the GUI.
Loading AmeliaView
The easiest way to load AmeliaView is to open an R session and type
the following two commands:
library(Amelia)
AmeliaView()
This will bring up the AmeliaView window on any platform.
AmeliaView welcome screen
Loading data into AmeliaView
AmeliaView loads with a welcome screen that has buttons which can
load a data in many of the common formats. Each of these will bring up a
window for choosing your dataset. Note that these buttons are only a
subset of the possible ways to load data in AmeliaView. Under the File
menu (shown below), you will find more options, including the datasets
included in the package (africa
and
freetrade
). You will also find import commands for
Comma-Separated Values (.CSV), Tab-Delimited Text (.TXT), Stata v.5-10
(.DTA), SPSS (.DAT), and SAS Transport (.XPORT). Note that when using a
CSV file, Amelia assumes that your file has a header (that is, a row at
the top of the data indicating the variable names).
AmeliaView File and import menu.
You can also load data from an RData file. If the RData file contains
more than one data.frame
, a pop-up window will ask to you
find the dataset you would like to load. In the file menu, you can also
change the underlying working directory. This is where AmeliaView will
look for data by default and where it will save imputed datasets.
Variable Dashboard
Main variable dashboard in AmeliaView
Once a dataset is loaded, AmeliaView will show the variable
dashboard. In this mode, you will see a table of variables, with the
current options for each of them shown, along with a few summary
statistics. You can reorder this table by any of these columns by
clicking on the column headings. This might be helpful to, say, order
the variables by mean or amount of missingness.
Variable options via right-click menu on the
variable dashboard
You can set options for individual variables by the right-click
context menu or through the “Variables” menu. For instance, clicking
“Set as Time-Series Variable” will set the currently selected variable
in the dashboard as the time-series variable. Certain options are
disabled until other options are enabled. For instance, you cannot add a
lagged variable to the imputation until you have set the time-series
variable. Note that any factor
in the data is marked as a
ID variable by default, since a factor
cannot be included
in the imputation without being set as an ID variable, a nominal
variable, or the cross-section variable. If there is a
factor
that fails to meet one of these conditions, a red
flag will appear next to the variable name.
- Set as Time-Series Variable - Sets the currently
selected variable to the time-series variable. Disabled when more than
one variable is selected. Once this is set, you can add lags and leads
and add splines of time. The time-series variable will have a clock icon
next to it.
- Set as Cross-Section Variable - Sets the currently
selected variable to the cross-section variable. Disabled when more than
one variable is selected. Once this is set, you can interact the splines
of time with the cross-section. The cross-section variable will have a
person icon next to it.
- Unset as Time-Series Variable - Removes the
time-series status of the variable. This will remove any lags, leads, or
splines of time.
- Unset as Cross-Section Variable - Removes the
cross-section status of the variable. This will remove any intersection
of the splines of time and the cross-section.
- Add Lag/Lead - Adds versions of the selected
variables either lagged back (“lag”) or forward (“lead”).
- Remove Lag/Lead - Removes any lags or leads on the
selected variables.
- Plot Histogram of Selected - Plots a histogram of
the selected variables. This command will attempt to put all of the
histograms on one page, but if more than nine histograms are requested,
they will appear on multiple pages.
- Add Transformation… - Adds a transformation setting
for the selected variables. Note that each variable can only have one
transformation and the time-series and cross-section variables cannot be
transformed.
- Remove Transformation - Removes any transformation
for the selected variables.
- Add or Edit Bounds - Opens a dialog box to set
logical bounds for the selected variable.
Amelia Options
Options menu
The “Variable” menu and the variable dashboard are the place to set
variable-level options, but global options are set in the “Options”
menu. For more information on these options, see
vignette("using-amelia")
.
- Splines of Time with… - This option, if activated,
will have Ameliause flexible trends of time with the specified number of
knots in the imputation. The higher the number of knots the greater the
variation in the trend structure, yet it will take more degrees of
freedom to estimate.
- Interact with Cross-Section? - Include and
interaction of the cross-section with the time trends. This interaction
is way of allowing the trend of time to vary across cases as well. Using
a 0-level spline of time and interacting with the cross section is the
equivalent of using a fixed effects.
- Add Observational Priors… - Brings a dialog window
to set prior beliefs about ranges for individual missing
observations.
- Numerical Options - Brings a dialog window to set
the tolerance of the EM algorithm, the seed of the random number
generator, the ridge prior for numerical stability, and the maximum
number of redraws for the logical bounds.
- Draw Missingness Map - Draws a missingness
map.
- Output File Options - Bring a dialog to set the
stub of the prefix of the imputed data files and the number of
imputations. If you set the prefix to
mydata
, your output
files will be mydata1.csv, mydata2.csv...
etc.
- Output File Type - Sets the format of imputed data.
If you would like to not save any output data sets (if you wanted, for
instance, to simply look at diagnostics), set this option to “(no
save).” Currently, you can save the output data as: Comma Separated
Values (.CSV), Tab Delimited Text (.TXT), Stata (.DTA), R save object
(.RData), or to hold it in R memory. This last option will only work if
you have called AmeliaView from an R session and want to return to the R
command line to work with the output. Its name in R workspace will be
the file prefix. The stacked version of the Stata output will work with
their built-in
mi
tools.
Numerical options
Numerical options menu
Seed - Sets the seed for the random number
generator used by Amelia. Useful if you need to have the same output
twice.
Tolerance - Adjust the level of tolerance that
Amelia uses to check convergence of the EM algorithm. In very large
datasets, if your imputation chains run a long time without converging,
increasing the tolerance will allow a lower threshold to judge
convergence and end chains after fewer iterations.
Empirical Prior - A prior that adds observations
to your data in order to shrink the covariances. A useful place to start
is around 0.5% of the total number of observations in the
dataset.
Maximum Resample for Bounds - Amelia fits
logical bounds by rejecting any draws that do not fall within the
bounds. This value sets the number of times Amelia should attempt to
resample to fit the bounds before setting the imputation to the
bound.
Add Distributional Prior
Detail for Add Distributional Prior dialog
- Current Priors - A table of current priors in
distributional form, with the variable and case name. You can remove
priors by selecting them and using the right-click context menu.
- Case - Select the case name or number you wish to
set the prior about. You can also choose to make the prior for the
entire variable, which will set the prior for any missing cell in that
variable. The case names are generated from the row name of the
observation, the value of the cross-section variable of the observation
and the value of the time series variable of the observation.
- Variable - The variable associated with the prior
you would like specify. The list provided only shows the missing
variables for the currently selected observation.
1.Mean - The mean value of the prior. The textbox will
not accept letters or out of place punctuation.
- Standard Deviation - The standard deviation of the
prior. The textbox will only accept positive non-zero values.
Add Range Prior
Detail for Add Range Prior dialog
- Case - Select the case name or number you wish to
set the prior about. You can also choose to make the prior for the
entire variable, which will set the prior for any missing cell in that
variable. The case names are generated from the row name of the
observation, the value of the cross-section variable of the observation
and the value of the time series variable of the observation.
- Variable - The variable associated with the prior
you would like specify. The list provided only shows the missing
variables for the currently selected observation.
- Minimum - The minimum value of the prior. The
textbox will not accept letters or out of place punctuation.
- Maximum - The maximum value of the prior. The
textbox will not accept letters or out of place punctuation.
- Confidence - The confidence level of the prior.
This should be between 0 and 1, non-inclusive. This value represents how
certain your priors are. This value cannot be 1, even if you are
absolutely certain of a give range. This is used to convert the range
into an appropriate distributional prior.
Imputing and checking diagnostics
Output log showing Amelia output for a
successful imputation.
Once you have set all the relevant options, you can impute your data
by clicking the “Impute!” button in the toolbar. In the bottom right
corner of the window, you will see a progress bar that indicates the
progress of the imputations. For large datasets this could take some
time. Once the imputations are complete, you should see a “Successful
Imputation!” message appear where the progress bar was. You can click on
this message to open the folder containing the imputed datasets.
If there was an error during the imputation, the output log will
pop-up and give you the error message along with some information about
how to fix the problem. Once you have fixed the problem, simply click
“Impute!” again. Even if there was no error, you may want to view the
output log to see how Ameliaran. To do so, simply click the “Show Output
Log” button. The log also shows the call to the amelia()
function in R. You can use this code snippet to run the same imputation
from the R command line. You will have to replace the x
argument in the amelia()
call to the name of you dataset in
the R session.
Diagnostics Dialog
Detail for the Diagnostics dialog
Upon the successful completion of an imputation, the diagnostics menu
will become available. Here you can use all of the diagnostics available
at the command-line.
- Compare Plots - This will display the relative
densities of the observed (red) and imputed (black) data. The density of
the imputed values are the average imputations across all of the imputed
datasets.
- Overimpute - This will run Ameliaon the full data
with one cell of the chosen variable artificially set to missing and
then check the result of that imputation against the truth. The
resulting plot will plot average imputations against true values along
with 90% confidence intervals. These are plotted over a \(y=x\) line for visual inspection of the
imputation model.
- Number of overdispersions - When running the
overdispersion diagnostic, you need to run the imputation algorithm from
several overdispersed starting points in order to get a clear idea of
how the chain are converging. Enter the number of imputations here.
- Number of dimensions - The overdispersion
diagnostic must reduce the dimensionality of the paths of the imputation
algorithm to either one or two dimensions due to graphical
restraints.
- Overdisperse - Run overdispersion diagnostic to
visually inspect the convergence of the Amelia algorithm from multiple
start values that are drawn randomly.
Sessions
It is often useful to save a session of AmeliaView to save time if
you have impute the same data again. Using the Save
Session button will do just that, saving all of the current
settings (including the original and any imputed data) to an RData file.
You can then reload your session, on the same computer or any other,
simply by clicking the Load Session button and finding
the relevant RData file. All of the settings will be restored, including
any completed imputations. Thus, if you save the session after imputing,
you can always load up those imputations and view their diagnostics
using the sessions feature of AmeliaView.