---
title: "Using Amelia"
date: "`r Sys.Date()`"
link-citations: yes
bibliography: amelia.bib
output:
  rmarkdown::html_vignette:
    keep_md: true
vignette: >
  %\VignetteIndexEntry{Using Amelia}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, echo = FALSE, include = FALSE}
knitr::opts_chunk$set(fig.width = 5, fig.height = 4, fig.align = "center")
options(digits = 4, show.signif.stars = FALSE)
set.seed(12345)
```


## Data 

We now demonstrate how to use Amelia using data from @MilKub05 which studies the effect of democracy on trade policy. For the purposes of this user's guide, we will use a subset restricted to nine developing countries in Asia from 1980 to 1999[^freetrade]. This dataset includes 9 variables:

| Variable   | Description                                         |
|:-----------|:----------------------------------------------------|
| `year`     | year                                                |
| `country`  | country                                             |
| `tariff`   | average tariff rates                                |
| `polity`   | Polity IV Score[^polity]                            |
| `pop`      | total population                                    |
| `gdp.pc`   | gross domestic product per capita                   |
| `intresmi` | gross international reserves                        |
| `signed`   | dummy variable if signed an IMF agreement that year |
| `fivop`    | measure of financial openness                       |
| `usheg`    | measure of US hegemony[^hegemony]                   |

 These variables correspond to the variables used in the analysis model of @MilKub05 in table 2.

[^freetrade]: We have artificially addedsome missingness to these data for presentational purposes. You can access the original data at
  [https://hvmilner.scholar.princeton.edu/data](https://hvmilner.scholar.princeton.edu/data).

[^polity]: The Polity score is a number between -10 and 10 indicating how democratic a country is. A fully autocratic country would be a -10 while a fully democratic country would be 1 10.

[^hegemony]: This measure of US hegemony is the US imports and exports as a percent of the world total imports and exports.

We first load the Amelia and the data:

```{r load_data, results = "hide"}
library(Amelia)
data(freetrade)
```



We can check the summary statistics of the data to see that there is missingness on many of the variables:

```{r summarize_data}
summary(freetrade)
```

In the presence of missing data, most statistical packages use *listwise deletion*, which removes any row that contains a missing value from the analysis. Using the base model of @MilKub05 Table 2, we run a simple linear model in R, which uses listwise deletion:

```{r mk_lm}
summary(lm(tariff ~ polity + pop + gdp.pc + year + country,
          data = freetrade))
```


Note that 60 of the 171 original observations are deleted due to missingness. These observations, however, are partially observed, and contain valuable information about the relationships between those variables which are present in the partially completed observations. Multiple imputation will help us retrieve that information and make better, more efficient, inferences.


## Multiple Imputation


When performing multiple imputation, the first step is to identify the variables to include in the imputation model. It is crucial to include at least as much information as will be used in the analysis model. That is, any variable that will be in the analysis model should also be in the imputation model. This includes any transformations or interactions of variables that will appear in the analysis model.

In fact, it is often useful to add more information to the imputation model than will be present when the analysis is run. Since imputation is predictive, any variables that would increase predictive power should be included in the model, even if including them in the analysis model would produce bias in estimating a causal effect (such as for post-treatment variables) or collinearity would preclude determining which variable had a relationship with the dependent variable (such as including multiple alternate measures of GDP). In our case, we include all the variables in `freetrade` in the imputation model, even though our analysis model focuses on `polity`, `pop` and `gdp.pc`. We're not incorporating time or spatial data yet, but we do below.

To create multiple imputations in Amelia, we can simply run
```{r amelia}
a.out <- amelia(freetrade, m = 5, ts = "year", cs = "country")
a.out
```

Note that our example dataset is deliberately small both in variables and in cross-sectional elements. Typical datasets may often have hundreds or possibly a couple thousand steps to the EM algorithm. Long chains should remind the analyst to consider whether transformations of the variables would more closely fit the multivariate normal assumptions of the model (correct but omitted transformations will shorten the number of steps and improve the fit of the imputations), but do not necessarily denote problems with the imputation model.

The output gives some information about how the algorithm ran. Each of the imputed datasets is now in the list `a.out$imputations`. Thus, we could plot a histogram of the `tariff` variable from the 3rd imputation,

```{r}
hist(a.out$imputations[[3]]$tariff, col = "grey", border = "white")
```

### Saving imputed datasets

If you need to save your imputed datasets, one direct method is to save the output list from `amelia`,

```{r save, eval = FALSE}
save(a.out, file = "imputations.RData")
```

As in the previous example, the ith imputed datasets can be retrieved from this list as `a.out$imputations[[i]]`.

In addition, you can save each of the imputed datasets to its own file using the `write.amelia()` command,

```{r write_amelia, eval = FALSE}
write.amelia(obj = a.out, file.stem = "outdata")
```

This will create one comma-separated value file for each imputed dataset in the following manner:

    outdata1.csv
    outdata2.csv
    outdata3.csv
    outdata4.csv
    outdata5.csv

The `write.amelia` function can also save files in tab-delimited and Stata (`.dta`) file formats. For instance, to save Stata files, simply change the `format` argument to `"dta"`,


```{r write_dta, eval = FALSE}
write.amelia(obj = a.out, file.stem = "outdata", format = "dta")
```

Additionally, `write.amelia()` can create a "stacked" version of the imputed dataset which stacks each imputed dataset on top of one another. This can be done by setting the \code{separate} argument to `FALSE`. The resulting matrix is of size $(N \cdot m) \times p$ if the original dataset is excluded (`orig.data = FALSE`) and of size $(N \cdot (m+1))\times p$ if it is included (`orig.data = TRUE`). The stacked dataset will include a variable (set with `impvar`) that indicates to which imputed dataset the observation belongs.

## Combining multiple calls to `amelia()`

The EMB algorithm is what computer scientists call *embarrassingly parallel*, meaning that it is simple to separate each imputation into parallel processes. With Amelia it is simple to run subsets of the imputations on different machines and then combine them after the imputation for use in analysis model. This allows for a huge increase in the speed of the algorithm.

Output lists from different Amelia runs can be combined together into a new list. For instance, suppose that we wanted to add another ten imputed datasets to our earlier call to `amelia()`. First, run the function to get these additional imputations,

```{r more_amelia}
a.out.more <- amelia(freetrade, m = 10, ts = "year", cs = "country", p2s = 0)
a.out.more
```

then combine this output with our original output using the `ameliabind()` function,

```{r ameliabind}
a.out.more <- ameliabind(a.out, a.out.more)
a.out.more
```

This function binds the two outputs into the same output so that you can pass the combined imputations easily to analysis models and diagnostics. Note that `a.out.more` now has a total of 15 imputations.

A simple way to execute a parallel processing scheme with Amelia would be to run `amelia()` with `m` set to 1 on $m$ different machines or processors, save each output using the `save()` function, load them all on the same R session using `load()` command and then combine them using `ameliabind()`. In order to do this, however, make sure to name each of the outputs a different name so that they do not overwrite each other when loading into the same R session. Also, some parallel environments will dump all generated files into a common directory, where they may overwrite each other. If it is convenient in a parallel environment to run a large number of `amelia()` calls from a single piece of code, one useful way to avoid overwriting is to create the `file.stem` with a random suffix. For example:

```{r rand_stem, eval = FALSE}
b <- round(runif(1, min = 1111, max = 9999))
random.name <- paste("am", b, sep = "")
amelia <- write.amelia(obj = a.out, file.stem = random.name)
```




### Screen output

Screen output can be adjusted with the "print to screen" argument,
`p2s`.  At a value of 0, no screen printing will occur.  This may be
useful in large jobs or simulations where a very large number of
imputation models may be required.  The default value of 1, lists each
bootstrap, and displays the number of iterations required to reach
convergence in that bootstrapped dataset.  The value of 2 gives more
thorough screen output, including, at each iteration, the number of
parameters that have significantly changed since the last iteration. This
may be useful when the EM chain length is very long, as it can provide an
intuition for many parameters still need to converge in the EM chain, and
a sense of the time remaining.  However, it is worth noting that the last
several parameters can often take a significant fraction of the total
number of iterations to converge.  Setting `p2s` to 2 will also
generate information on how EM algorithm is behaving, such as a `!`
when the current estimated complete data covariance matrix is not
invertible and a `*` when the likelihood has not monotonically
increased in that step. Having many of these two symbols in the screen
output is an indication of a problematic imputation
model. Problems of non-invertible matrices often mean that current
  guess for the covariance matrix is singular. This is a sign that there
  may be two highly correlated variables in the model. One way to resolve
  is to use a ridge prior (see \@ref(sec_prior)).

An example of the output when `p2s` is 2 would be

```{r p2s}
a.out.p2s <- amelia(freetrade, m = 1, ts = "year", cs = "country", p2s = 2)
```


## Parallel Imputation {#sec:parallel}

Each imputation in the above EMB algorithm is completely independent of any other imputation, a property called embarrassingly parallel. This type of approach can take advantage of the multiple-core infrastructure of modern CPUs. Each core in a multi-core processor can execute independent operations in parallel. Amelia can utilize this parallel processing internally via the `parallel` and the `ncpus` arguments. The `parallel` argument sets the parallel processing backend, either with `"multicore"` or `"snow"` (or `"no"` for no parallel processing). The `"multicore"` backend is not available on Windows systems, but tends to be quicker at parallel processing. On a Windows system, the `"snow"` backend provides parallel processing through a cluster of worker processes across the CPUs. You can set the default for this argument using the `"amelia.parallel"` option. This allows you to run Amelia in parallel as the default for an entire R session without setting arguments in the `amelia()` call.

For each of the parallel backends, Amelia requires a number of CPUs to use in parallel. This can be set using the `ncpus` argument. It can be higher than the number of physical cores in the system if hyperthreading or other technologies are available. You can use the `parallel::detectCores()` function to determine how many cores are available on your machine. The default for this argument can be set using the `"amelia.ncpus"` option.

On Unix-alike systems (such as macOS and Linux distributions), the `"multicore"` backend automatically sets up and stops the parallel workers by forking the process. On Windows, the `"snow"` backend requires more attention. Amelia will attempt to create a parallel cluster of worker processes (since Windows systems cannot fork a process) and will stop this cluster after the imputations are complete. Alternatively, Amelia also has a `cl` argument, which accepts a predefined cluster made using the `parallel::makePSOCKcluster()`. For more information about parallel processing in R, see the documentation for the `parallel` package that ships along with R or the CRAN Task View on [Parallel Computing with R](https://cran.r-project.org/view=HighPerformanceComputing)


## Improving Imputations via Transformations {#sec:trans}


Social science data commonly includes variables that fail to fit to a multivariate normal distribution. Indeed, numerous models have been introduced specifically to deal with the problems they present. As it turns out, much evidence in the literature [discussed in @KinHonJos01] indicates that the multivariate normal model used in Amelia usually works well for the imputation stage even when discrete or non-normal variables are included and when the analysis stage involves these limited dependent variable models. Nevertheless, Amelia includes some limited capacity to deal directly with ordinal and nominal variables and to modify variables that require other transformations. In general nominal and log transform variables should be declared to Amelia, whereas ordinal (including dichotomous) variables often need not be, as described below. (For harder cases, see [@Schafer97], for specialized MCMC-based imputation models for discrete variables.)

Although these transformations are taken internally on these variables to better fit the data to the multivariate normal assumptions of the imputation model, all the imputations that are created will be returned in the original untransformed form of the data. If the user has already performed transformations on their data (such as by taking a log or square root prior to feeding the data to `amelia()`) these do not need to be declared, as that would result in the transformation occurring *doubly* in the imputation model. The fully imputed data sets that are returned will always be in the form of the original data that is passed to the `amelia()` routine.


### Ordinal {#sec:ord}

In much statistical research, researchers treat independent ordinal (including dichotomous) variables as if they were really continuous. If the analysis model to be employed is of this type, then nothing extra is required of the of the imputation model. Users are advised to allow Amelia to impute non-integer values for any missing data, and to use these non-integer values in their analysis. Sometimes this makes sense, and sometimes this defies intuition. One particular imputation of 2.35 for a missing value on a seven point scale carries the intuition that the respondent is between a 2 and a 3 and most probably would have responded 2 had the data been observed. This is easier to accept than an imputation of 0.79 for a dichotomous variable where a zero represents a male and a one represents a female respondent. However, in both cases the non-integer imputations carry more information about the underlying distribution than would be carried if we were to force the imputations to be integers. Thus whenever the analysis model permits, missing ordinal observations should be allowed to take on continuously valued imputations.

In the `freetrade` data, one such ordinal variable is `polity` which ranges from -10 (full autocracy) to 10 (full democracy). If we tabulate this variable from one of the imputed datasets,

```{r polity_tab}
table(a.out$imputations[[3]]$polity)
```

we can see that there is one imputation between -4 and -3 and one imputation between 6 and 7. Again, the interpretation of these values is rather straightforward even if they are not strictly in the coding of the original Polity data.

Often, however, analysis models require some variables to be strictly ordinal, as for example, when the dependent variable will be modeled in a logistical or Poisson regression. Imputations for variables set as ordinal are created by taking the continuously valued imputation and using an appropriately scaled version of this as the probability of success in a binomial distribution. The draw from this binomial distribution is then translated back into one of the ordinal categories.

For our data we can simply add `polity` to the `ords` argument:

```{r polity_ord}
a.out1 <- amelia(freetrade, m = 5, ts = "year", cs = "country", ords =
                 "polity", p2s = 0)
table(a.out1$imputations[[3]]$polity)
```

Now, we can see that all of the imputations fall into one of the original polity categories.


### Nominal {#sec:nom}

Nominal variables[^binary] must be treated quite differently than ordinal variables. Any multinomial variables in the data set (such as religion coded 1 for Catholic, 2 for Jewish, and 3 for Protestant) must be specified to Amelia. In our \code{freetrade} dataset, we have `signed` which is 1 if a country signed an IMF agreement in that year and 0 if it did not. Of course, our first imputation did not limit the imputations to these two categories

```{r binary_tab}
table(a.out1$imputations[[3]]$signed)
```

In order to fix this for a $p$-category multinomial variable, Amelia will determine $p$ (as long as your data contain at least one value in each category), and substitute $ p-1$ binary variables to specify each possible category. These new $p-1$ variables will be treated as the other variables in the multivariate normal imputation method chosen, and receive continuous imputations. These continuously valued imputations will then be appropriately scaled into probabilities for each of the $p$ possible categories, and one of these categories will be drawn, where upon the original $p$-category multinomial variable will be reconstructed and returned to the user. Thus all imputations will be appropriately multinomial.

[^binary]: Dichotomous (two category) variables are a special case of nominal variables. For these variables, the nominal and ordinal methods of transformation in Amelia agree.

For our data we can simply add `signed` to the `noms` argument:

```{r noms}
a.out2 <- amelia(freetrade, m = 5, ts = "year", cs = "country",
                 noms = "signed", p2s = 0)
table(a.out2$imputations[[3]]$signed)
```

Note that Amelia can only fit imputations into categories that exist in the original data. Thus, if there was a third category of signed, say 2, that corresponded to a different kind of IMF agreement, but it never occurred in the original data, Amelia could not match imputations to it.

Since Amelia properly treats a $p$-category multinomial variable as $p-1$ variables, one should understand the number of parameters that are quickly accumulating if many multinomial variables are being used. If the square of the number of real and constructed variables is large relative to the number of observations, it is useful to use a ridge prior as in section \@ref(sec_prior).


### Natural log {#sec:log}

If one of your variables is heavily skewed or has outliers that may alter the imputation in an unwanted way, you can use a natural logarithm transformation of that variable in order to normalize its distribution. This transformed distribution helps Amelia to avoid imputing values that depend too heavily on outlying data points. Log transformations are common in expenditure and economic variables where we have strong beliefs that the marginal relationship between two variables decreases as we move across the range.

For instance, we can show the `tariff` variable clearly has positive (or, right) skew while its natural log transformation has a roughly normal distribution.

```{r tarrif_hist}
hist(freetrade$tariff, col="grey", border="white")
hist(log(freetrade$tariff), col="grey", border="white")
```


### Square root {#sec:sqrt}

Event count data is often heavily skewed and has nonlinear relationships with other variables. One common transformation to tailor the linear model to count data is to take the square roots of the counts. This is a transformation that can be set as an option in Amelia.

### Logistic {#sec:lgstc}

Proportional data is sharply bounded between 0 and 1. A logistic transformation is one possible option in Amelia to make the distribution symmetric and relatively unbounded.

## Identification Variables {#sec:idvars}

Datasets often contain identification variables, such as country names, respondent numbers, or other identification numbers, codes or abbreviations. Sometimes these are text and sometimes these are numeric. Often it is not appropriate to include these variables in the imputation model, but it is useful to have them remain in the imputed datasets (However, there are models that would include the ID variables in the imputation model, such as fixed effects model for data with repeated observations of the same countries). Identification variables which are not to be included in the imputation model can be identified with the argument `idvars`. These variables will not be used in the imputation model, but will be kept in the imputed datasets.

If the `year` and `country` contained no information except labels, we could omit them from the imputation:

```{r idvars}
amelia(freetrade, idvars = c("year", "country"))
```

Note that Amelia will return with an error if your dataset contains a factor or character variable that is not marked as a nominal or identification variable. Thus, if we were to omit the factor `country` from the `cs` or `idvars` arguments, we would receive an error:

```{r idvars_error}
a.out2 <- amelia(freetrade, idvars = c("year"))
```

In order to conserve memory, it is wise to remove unnecessary variables from a data set before loading it into Amelia. The only variables you should include in your data when running Amelia are variables you will use in the analysis stage and those variables that will help in the imputation model. While it may be tempting to simply mark unneeded variables as IDs, it only serves to waste memory and slow down the imputation procedure.


## Time Series, or Time Series Cross Sectional Data {#sec:tscs}


Many variables that are recorded over time within a cross-sectional unit are observed to vary smoothly over time. In such cases, knowing the observed values of observations close in time to any missing value may enormously aid the imputation of that value. However, the exact pattern may vary over time within any cross-section. There may be periods of growth, stability, or decline; in each of which the observed values would be used in a different fashion to impute missing values. Also, these patterns may vary enormously across different cross-sections, or may exist in some and not others. Amelia can build a general model of patterns within variables across time by creating a sequence of polynomials of the time index. If, for example, tariffs vary smoothly over time, then we make the modeling assumption that there exists some polynomial that describes the economy in cross-sectional unit $i$ at time $t$ as:

\[
\textrm{tariff}_{ti} = \beta_0 + \beta_1 t + \beta_1 t^2 + \beta_1 t^3 \ldots
\]

And thus if we include enough higher order terms of time then the pattern between observed values of the tariff rate can be estimated. Amelia will create polynomials of time up to the user defined $k$-th order, ($k\leq3$).

We can implement this with the `ts` and `polytime` arguments. If we thought that a second-order polynomial would help predict we could run

```{r polytime, results = "hide"}
a.out2 <- amelia(freetrade, ts = "year", cs = "country", polytime = 2)
```

With this input, Amelia will add covariates to the model that correspond to time and its polynomials. These covariates will help better predict the missing values.

If cross-sectional units are specified these polynomials can be interacted with the cross-section unit to allow the patterns over time to vary between cross-sectional units. Unless you strongly believe all units have the same patterns over time in all variables (including the same constant term), this is a reasonable setting. When $k$ is set to 0, this interaction simply results in a model of *fixed effects* where every unit has a uniquely estimated constant term. Amelia does not smooth the observed data, and only uses this functional form, or one you choose, with all the other variables in the analysis and the uncertainty of the prediction, to impute the missing values.

In order to impute with trends specific to each cross-sectional unit, we can set `intercs` to `TRUE`:

```{r intercs, results = "hide"}
a.out.time <- amelia(freetrade, ts = "year", cs = "country", polytime = 1,
                 intercs = TRUE, p2s = 2)
```

Note that attempting to use `polytime` without the `ts` argument, or `intercs` without the `cs` argument will result in an error.

Using the `tscsPlot()` function (discussed below), we can see that we have a much better prediction about the missing values when incorporating time than when we omit it:

```{r tcomp1}
tscsPlot(a.out, cs = "Malaysia", main = "Malaysia (no time settings)",
         var = "tariff", ylim = c(-10, 60))

tscsPlot(a.out.time, cs = "Malaysia", main = "Malaysia (with time settings)",
         var = "tariff", ylim = c(-10, 60))
```



### Lags and leads {#sec:lags}

An alternative way of handling time-series information is to include lags and leads of certain variables into the imputation model. *Lags* are variables that take the value of another variable in the previous time period while *leads* take the value of another variable in the next time period. Many analysis models use lagged variables to deal with issues of endogeneity, thus using leads may seems strange. It is important to remember, however, that imputation models are predictive, not causal. Thus, since both past and future values of a variable are likely correlated with the present value, both lags and leads should improve the model.

If we wanted to include lags and leads of tariffs, for instance, we would simply pass this to the `lags` and `leads` arguments:

```{r lags_leads}
a.out2 <- amelia(freetrade, ts = "year", cs = "country", lags = "tariff",
                 leads = "tariff")
```


## Including Prior Information


Amelia has a number of methods of setting priors within the imputation model. Two of these are commonly used and discussed below, ridge priors and observational priors.

### Ridge priors for high missingness, Small samples, or large correlations {#sec_prior}

When the data to be analyzed contain a high degree of missingness or very strong correlations among the variables, or when the number of observations is only slightly greater than the number of parameters $p(p+3)/2$ (where $p$ is the number of variables), results from your analysis model will be more dependent on the choice of imputation model. This suggests more testing in these cases of alternative specifications under Amelia. This can happen when using the polynomials of time interacted with the cross section are included in the imputation model. For example, in our data, if we used a polynomial of degree 2 with unit-specific trends and there are 9 countries, it would add $3 \times 9 - 1= 17$ more variables to the imputation model (dropping one of the fixed effects for identification). When these are added, the EM algorithm can become unstable. You can detect this by inspecting the screen output under `p2s = 2` or by observing that the number iterations per imputation are very divergent. 

In these circumstances, we recommend adding a ridge prior which will help with numerical stability by shrinking the covariances among the variables toward zero without changing the means or variances. This can be done by including the `empri` argument. Including this prior as a positive number is roughly equivalent to adding `empri` artificial observations to the data set with the same means and variances as the existing data but with zero covariances. Thus, increasing the `empri` setting results in more shrinkage of the covariances, thus putting more a priori structure on the estimation problem: like many Bayesian methods, it reduces variance in return for an increase in bias that one hopes does not overwhelm the advantages in efficiency. In general, we suggest keeping the value on this prior relatively small and increase it only when necessary. A recommendation of 0.5 to 1 percent of the number of observations, $n$, is a reasonable starting value, and often useful in large datasets to add some numerical stability. For example, in a dataset of two thousand observations, this would translate to a prior value of 10 or 20 respectively. A prior of up to 5 percent is moderate in most applications and 10 percent is reasonable upper bound.

For our data, it is easy to code up a 1 percent ridge prior:

```{r empri}
a.out.time2 <- amelia(freetrade, ts = "year", cs = "country", polytime = 1,
                 intercs = TRUE, p2s = 0, empri = .01 * nrow(freetrade))
a.out.time2
```

### Observation-level priors {#sec:obspri}

Researchers often have additional prior information about missing data values based on previous research, academic consensus, or personal experience. Amelia can incorporate this information to produce vastly improved imputations. The Amelia algorithm allows users to include informative Bayesian priors about individual missing data cells instead of the more general model parameters, many of which have little direct meaning.

The incorporation of priors follows basic Bayesian analysis where the imputation turns out to be a weighted average of the model-based imputation and the prior mean, where the weights are functions of the relative strength of the data and prior: when the model predicts very well, the imputation will down-weight the prior, and vice versa [@HonKin10].

The priors about individual observations should describe the analyst's belief about the distribution of the missing data cell. This can either take the form of a mean and a standard deviation or a confidence interval. For instance, we might know that 1986 tariff rates in Thailand around 40%, but we have some uncertainty as to the exact value. Our prior belief about the distribution of the missing data cell, then, centers on 40 with a standard deviation that reflects the amount of uncertainty we have about our prior belief.

To input priors you must build a priors matrix with either four or five columns. Each row of the matrix represents a prior on either one observation or one variable. In any row, the entry in the first column is the row of the observation and the entry is the second column is the column of the observation. In the four column priors matrix the third and fourth columns are the mean and standard deviation of the prior distribution of the missing value.

For instance, suppose that we had some expert prior information about tariff rates in Thailand. We know from the data that Thailand is missing tariff rates in many years,

```{r thailand}
freetrade[freetrade$country == "Thailand", c("year", "country", "tariff")]
```

Suppose that we had expert information that tariff rates were roughly 40% in Thailand between 1986 and 1988 with about a 6% margin of error. This corresponds to a standard deviation of about 3. In order to include this information, we must form the priors matrix:

```{r build_prior}
pr <- matrix(
  c(158, 159, 160, 3, 3, 3, 40, 40, 40, 3, 3, 3),
  nrow = 3, ncol = 4
)
pr
```


The first column of this matrix corresponds to the row numbers of Thailand in these three years, the second column refers to the column number of `tariff` in the data and the last two columns refer to the actual prior. Once we have this matrix, we can pass it to `amelia()`,

```{r amelia_prior}
a.out.pr <- amelia(freetrade, ts = "year", cs = "country", priors = pr)
```

In the five column matrix, the last three columns describe a confidence range of the data. The columns are a lower bound, an upper bound, and a confidence level between 0 and 1, exclusive. Whichever format you choose, it must be consistent across the entire matrix. We could get roughly the same prior as above by utilizing this method. Our margin of error implies that we would want imputations between 34 and 46, so our matrix would be

```{r build_prior2}
pr.2 <- matrix(
  c(158, 159, 160, 3, 3, 3, 34, 34, 34, 46, 46, 46, 0.95, 0.95, 0.95),
  nrow = 3, ncol = 5
)
pr.2
```

These priors indicate that we are 95% confident that these missing values are in the range 34 to 46.

If a prior has the value 0 in the first column, this prior will be applied to all missing values in this variable, except for explicitly set priors. Thus, we could set a prior for the entire `tariff` variable of 20, but still keep the above specific priors with the following code:

```{r build_prior3}
pr.3 <- matrix(
  c(158, 159, 160, 0, 3, 3 , 3, 3, 40, 40, 40, 20, 3, 3, 3, 5),
  nrow = 4, ncol = 4)
pr.3
```


### Logical bounds

In some cases, variables in the social sciences have known logical bounds. Proportions must be between 0 and 1 and duration data must be greater than 0, for instance. Many of these logical bounds can be handled by using the correct transformation for that type of variable (see \@ref(sec:trans) for more details on the transformations handled by Amelia). In the occasional case that imputations must satisfy certain logical bounds not handled by these transformations, Amelia can take draws from a truncated normal distribution in order to achieve imputations that satisfy the bounds. Note, however, that this procedure imposes extremely strong restrictions on the imputations and can lead to lower variances than the imputation model implies. The mean value across all the imputed values of a missing cell is the best guess from the imputation model of that missing value. The variance of the distribution across imputed datasets correctly reflects the uncertainty in that imputation. It is often the mean imputed value that should conform to the any known bounds, even if individual imputations are drawn beyond those bounds. The mean imputed value can be checked with the diagnostics presented in the next section. In general, building a more predictive imputation model will lead to better imputations than imposing bounds.

Amelia implements these bounds by rejection sampling. When drawing the imputations from their posterior, we repeatedly resample until we have a draw that satisfies all of the logical constraints. You can set an upper limit on the number of times to resample with the `max.resample` arguments. Thus, if after `max.resample` draws, the imputations are still outside the bounds, Amelia will set the imputation at the edge of the bounds. Thus, if the bounds were 0 and 100 and all of the draws were negative, Amelia would simply impute 0.

As an extreme example, suppose that we know, for certain that tariff rates had to fall between 30 and 40. This, obviously, is not true, but we can generate imputations from this model. In order to specify these bounds, we need to generate a matrix of bounds to pass to the `bounds` argument. This matrix will have 3 columns: the first is the column for the bounded variable, the second is the lower bound and the third is the upper bound. Thus, to implement our bound on tariff rates (the 3rd column of the dataset), we would create the matrix,

```{r build_bounds}
bds <- matrix(c(3, 30, 40), nrow = 1, ncol = 3)
bds
```

which we can pass to the `bounds` argument to `amelia()`:

```{r amelia_bounds}
a.out.bds <- amelia(freetrade, ts = "year", cs = "country", bounds = bds,
                    max.resample = 1000)
```


The difference in results between the bounded and unbounded model are not obvious from the output, but inspection of the imputed tariff rates for Malaysia shows that there has been a drastic restriction of the imputations to the desired range:

```{r bounds_plot}
tscsPlot(a.out, cs = "Malaysia", main = "No logical bounds", var = "tariff",
         ylim = c(-10, 60))

tscsPlot(a.out.bds, cs = "Malaysia", main = "Bounded between 30 and 40",
         var = "tariff", ylim = c(-10, 60))
```


Again, analysts should be extremely cautious when using these bounds as they can seriously affect the inferences from the imputation model, as shown in this example. Even when logical bounds exist, we recommend simply imputing variables normally, as the violation of the logical bounds represents part of the true uncertainty of imputation.

## Post-imputations Transformations {#sec_postimptrans}

In many cases, it is useful to create transformations of the imputed variables for use in further analysis. For instance, one may want to create an interaction between two variables or perform a log-transformation on the imputed data. To do this, Amelia includes a `transform()` function for `amelia()` output that adds or overwrites variables in each of the imputed datasets. For instance, if we wanted to create a log-transformation of the `gdp.pc` variable, we could use the following command:

```{r amelia_transform}
a.out <- transform(a.out, lgdp = log(gdp.pc))
head(a.out$imputations[[1]][,c("country", "year","gdp.pc", "lgdp")])
```

To create an interaction between two variables, we could simply use:

```{r interaction}
a.out <- transform(a.out, pol_gdp = polity * gdp.pc)
```

Each transformation is recorded and the `summary()` command prints out each transformation that has been performed:

```{r sum_trans}
summary(a.out)
```

Note the updated output is almost exactly the same as the fresh `amelia()` output. You can pass the transformed output back to `amelia()` and it will add imputations and update these imputations with the transformations you have performed. 

## Analysis Models {#sec_analysis}

Imputation is most often a data processing step as opposed to a final model in of itself. To this end, it is easy to pass output from `amelia()` to other functions. The easiest and most integrated way to run an analysis model is to use the `with()` and `mi.combine()` functions. For example, in @MilKub05, the dependent variable was tariff rates. We can replicate table 5.1 from their analysis with the original data simply by running


```{r lm_lwd}
orig.model <- lm(tariff ~ polity + pop + gdp.pc + year + country, data = freetrade)
orig.model
```

Running the same model with imputed data is almost identical. We can run the `lm` within each imputed data set by using the `with()` function:


```{r lm_imp}
imp.models <- with(
  a.out,
  lm(tariff ~ polity + pop + gdp.pc + year + country)
)
imp.models[1:2]
```

The result here is simply a list of output of `lm()` applied to each imputed data set. We can combine the imputed estimates using the rules described in @KinHonJos01 and @Schafer97 with the `mi.combine()` function:


```{r mi_combine}
out <- mi.combine(imp.models, conf.int = TRUE)
out
```

The combination of the results depends on the [broom](https://broom.tidymodels.org) package and results can be combined if a `tidy()` method exists for the estimation function passed to `with()`. Other packages such as [Zelig](https://docs.zeligproject.org/index.html) can also combine imputed data sets across a number of statistical models. Furthermore, users can easily export their imputations using the `write.amelia()` function as described in \@ref(sec_saving) and use statistical packages other than R for the analysis model.

In addition to the resources available in R, users can draw on
Stata to implement their analysis models. As of version 11,
  Stata has built-in handling of multiply imputed datasets. In order
to utilize this functionality, simply export the "stacked"
imputations using the `write.amelia()` function:

```{r write_dta_stacked, eval = FALSE}
write.amelia(a.out, separate = FALSE, file.stem = "outdata", format = "dta")
```

Once this stacked dataset is open in Stata, you must tell Stata that
it is an imputed dataset using the \code{mi import flong} command:

```{stata eval = FALSE}
mi import flong, m(imp) id(year country) imp(tariff-usheg)
```

The command takes a few options: `m` designates the imputation
variable (set with `impvar` in `write.amelia()`), `id`
 sets the identifying varibles, and `imp` sets the variables that
 were imputed (or included in the imputation). The `tariff-usheg`
 indicates that Stata should treat the range of variables between
 `tariff` and `usheg` as imputed. Once we have set the
 dataset as imputed, we can use the built-in `mi` commands to
 analyze the data:

```{stata eval = FALSE}
mi estimate: reg tariff polity pop gdp_pc
```


```
Multiple-imputation estimates                     Imputations     =          5
Linear regression                                 Number of obs   =        171
                                                  Average RVI     =     1.4114
                                                  Complete DF     =        167
DF adjustment:   Small sample                     DF:     min     =      10.36
                                                          avg     =      18.81
                                                          max     =      37.62
Model F test:       Equal FMI                     F(   2,   10.4) =      15.50
Within VCE type:          OLS                     Prob > F        =     0.0008

------------------------------------------------------------------------------
      tariff |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      polity |  -.2058115   .3911049    -0.53   0.610    -1.072968    .6613452
         pop |   3.21e-08   8.72e-09     3.68   0.004     1.27e-08    5.14e-08
      gdp_pc |  -.0027561    .000644    -4.28   0.000    -.0040602   -.0014519
       _cons |   32.70461   2.660091    12.29   0.000     27.08917    38.32005
------------------------------------------------------------------------------
```


## The `amelia` class {#sec_out}

The output from the `amelia()` function is an instance of the S3 class `amelia`. Instances of the `amelia` class contain much more than simply the imputed datasets. The `mu` object of the class contains the posterior draws of the means of the complete data. The `covMatrices` contains the posterior draws of the covariance matrices of the complete data. Note that these correspond to the variables as they are sent to the EM algorithm. Namely, they refer to the variables after being transformed, centered and scaled.

The `iterHist` object is a list of `m` 3-column matrices. Each row of the matrices corresponds to an iteration of the EM algorithm. The first column indicates how many parameters had yet to converge at that iteration. The second column indicates if the EM algorithm made a step that decreased the number of converged parameters. The third column indicates whether the covariance matrix at this iteration was singular. Clearly, the last two columns are meant to indicate when the EM algorithm enters a problematic part of the parameter space.

## References
