The goal of the hbamr package is to enable users to efficiently perform Hierarchical Bayesian AldrichMcKelvey (HBAM) scaling in R. AldrichMcKelvey (AM) scaling is a method for estimating the ideological positions of survey respondents and political actors on a common scale using ideological survey data (Aldrich and McKelvey 1977). The hierarchical versions of the AM model included in this package outperform other versions both in terms of yielding meaningful posterior distributions for all respondent positions and in terms of recovering true respondent positions in simulations. The default model is described in an open access article (Bølstad 2023).
The package mainly fits models via the NUTS algorithm in rstan – a Markov chain Monte Carlo (MCMC) algorithm. However, it also offers a simplified model that can be fit using optimization to analyze large data sets quickly.
This vignette provides an overview of how to use the key functions in the hbamr package. The vignette walks through an applied example, showing how to prepare data, fit models, extract estimates, plot key results, and perform crossvalidation.
For illustration, we will use data from the 1980 American National
Election Study (ANES). This is the same data set that serves to
illustrate the original AM model in the basicspace
package. The data set is included in the hbamr package
and can be loaded by running data(LC1980)
.
The data set contains respondents’ placements of themselves and six stimuli on 7point LiberalConservative scales. The stimuli in question are: The Democratic and Republican parties, Democratic presidential candidate Jimmy Carter, Republican candidate Ronald Reagan, independent candidate (and former Republican) John B. Anderson, and Ted Kennedy (who challenged the incumbent Carter, but failed to win the Democratic nomination).
We load the data and recode missing values as follows:
library("hbamr")
data(LC1980)
LC1980[LC1980 == 0  LC1980 == 8  LC1980 == 9] < NA
self < LC1980[, 1]
stimuli < LC1980[, 1]
## Carter Reagan Kennedy Anderson Republicans Democrats
## 1 2 6 1 7 5 5
## 8 4 6 4 7 6 4
## 9 3 6 3 3 6 2
## 10 6 4 3 3 5 4
## 11 7 2 5 5 7 5
## 13 6 6 2 5 7 4
The function prep_data()
serves to prepare the data.
This function can be run ahead of fitting the models, or it can be run
implicitly as part of a single function call to fit the models (as shown
below). The function takes a vector of \(N\) ideological selfplacements and an
\(N \times J\) matrix of stimulus
placements. It applies a set of inclusion criteria, performs any
necessary data transformation, and returns a list of data suited for
sampling in rstan. The stimuli data are stored in a
vector as a longform sparse matrix. If the stimuli data include
columnnames, these will be preserved for later use.
Any missing data must be set to NA
before use. The
prep_data()
function allows the user to decide how many
missing values should be permitted per respondent by specifying the
argument allow_miss
. (The default is
allow_miss = 2
. Alternatively, the argument
req_valid
specifies how many valid observations to require
per respondent. The default is req_valid = J  allow_miss
,
but, if specified, req_valid
takes precedence.) Similarly,
the user may specify how may unique positions on the ideological scale
each respondent is required to have used when placing the stimuli in
order to be included in the analysis. The default is
req_unique = 2
, which means that respondents who place all
stimuli in exactly the same place will not be included.
The data provided to prep_data()
can be centered, but
they do not have to be: The function will detect uncentered data and
attempt to center these automatically, assuming that the highest and
lowest observed values in the data mark the extremes of the scale.
To use the prep_data()
function on the example data
using the default settings, we would run:
Users who want to keep other covariates for subsequent analysis, may
find it useful to run prep_data()
separately from the call
to fit the models. The list returned by this function includes the
logical vector keep
, which identifies the rows in the
original data that have been kept. If we had a data set x
containing covariates, and used prep_data()
to produce the
list dat
, then we could use x[dat$keep, ]
to
get a subset of x
corresponding to the data used in the
analysis. (The order of the individuals/rows in the data remains
unchanged by the functions in this package.)
The package provides several alternative models, which can be selected using the names below. Users who are unsure which model to use are advised to use the default HBAM model. If speed or sampling diagnostics are an issue, the HBAM_MINI model may provide a useful alternative.
HBAM is the default model, which allows for scale flipping and employs hierarchical priors on all individual level parameters. It also models heteroskedastic errors that vary by both individual and stimuli.
HBAM_2 uses different hyperpriors for the shift parameters of respondents with different selfplacements. In particular, the model estimates a separate mean hyperparameter for each selfplacement. This model avoids shrinking the shift parameters toward a common population mean, and may therefore fit better than HBAM if there are clear differences in average shifting across the scale of selfplacements. However, this model also tends to sample slower than the standard model.
HBAM_NE models the selfplacements as if they contain no error. The latent respondent positions are not treated as parameters, but rather calculated as function of the selfplacements and other individual level parameters. The respondents positions are not given a prior, which means the model relies on the likelihood function and the prior on beta to yield meaningful results for these positions. By assuming no error in the selfplacements, the model may underestimate the uncertainty in estimated respondents positions, while otherwise yielding very similar results to the standard HBAM model. In contrast to the standard model, the estimated respondent positions from this model will not exhibit any shrinkage, which for some purposes may be desirable, as the results may better represent the true distances between respondents and stimuli. This model also runs somewhat faster than the standard HBAM model.
HBAM_HM assumes the prediction errors in the stimuli placements to be homoskedastic. This simplified model should normally produce very similar results to the HBAM model, and it runs somewhat faster.
HBAM_MINI combines the characteristics of HBAM_NE and HBAM_HM: It models the selfplacements as if they contain no error and assumes the prediction errors in the stimuli placements to be homoskedastic. This is the simplest model provided in this package that still retains all key features of the HBAM model. It is also the fastest HBAM variant in this package – sampling two to three times faster than the standard HBAM model, while typically yielding very similar point estimates. For large data sets, this model may provide a reasonable compromise between model complexity and estimation speed.
HBAM_0 does not allow for scale flipping. This may be useful if there are truly zero cases of scale flipping in the data. Such scenarios can be created artificially, but may also arise in real data. For example, expert surveys appear unlikely to contain many instances of scale flipping.
HBAM_R incorporates the rationalization component of
the ISR model by Bølstad (2020). This
model requires additional data to be supplied to the
prep_data()
function: An \(N
\times J\) matrix of stimuli ratings from the respondents,
supplied as the argument pref
. The rationalization part of
the model is simplified relative to the original ISR model: The
direction in which respondents move disfavored stimuli is estimated as a
common expectation for each possible selfplacement on the scale.
HBAM_R_MINI combines the features of the HBAM_R model with the lightweight features of the HBAM_MINI model to achieve faster sampling compared to HBAM_R.
FBAM_MINI is a version of the HBAM_MINI model with fixed hyperparameters to allow fitting via optimization rather than MCMC – which can be useful for large data sets. The hyperparameters have been set to realistic values based on analyses of ANES data. As with the other models, scaledependent priors are automatically adjusted to the length of the survey scale.
BAM is an unpooled model, similar to the JAGS version introduced by Hare et al. (2015). This model is mainly provided to offer a baseline for model comparisons. While it is simple and fast, this model tends to overfit the data and produce invalid posterior distributions for some respondent positions (Bølstad 2023).
Some of these models can also be used in situations where
selfplacements are not available and the only goal is to estimate
stimulus positions. This can be achieved by supplying a vector of zeros
(or random data) instead of real selfplacements:
self = rep(0, nrow(stimuli))
. The HBAM_NE and HBAM_MINI
models are then the relevant alternatives, as the other HBAM variants
will include superfluous parameters (and will not sample properly with
zero variance in the supplied selfplacement data).
The hbam()
function can be used to fit all models in
this package and it returns a stanfit
object. The default
model is HBAM, while other models can be specified via the argument
model
. For each model, hbam()
selects a
suitable function to produce initial values for the sampling
algorithm.
Unless the user specifies the argument
prep_data = FALSE
, hbam()
will implicitly run
prep_data()
. It therefore takes the same arguments as the
prep_data()
function (i.e. self
,
stimuli
, allow_miss
, req_valid
,
and req_unique
).
To fit the HBAM model using the default settings, we would run:
To fit the HBAM_MINI model while requiring complete data for all respondents, we would run:
If we wanted to run the prep_data()
function separately
before fitting the model, we would specify
prep_data = FALSE
. The hbam()
function then
expects the argument data
instead of self
and
stimuli
. The data
argument should be a list
produced by prep_data()
:
hbam()
uses rstan::sampling()
, and any
additional arguments to hbam()
will be passed on to the
sampling function. By default, hbam()
will run 4 chains and
detect the number of available CPU cores. If possible,
hbam()
will use as many cores as there are chains. The
other sampling defaults are: warmup = 1000
,
iter = 4000
, and thin = 3
. These settings can
all be overridden in the hbam()
call.
For very large data sets, optimization can be a useful and much faster alternative to MCMC. However, the optimization feature provided in this package only provides maximum a posteriori (MAP) point estimates.
The fbam()
function fits the FBAM_MINI model using
rstan::optimizing()
. The fbam()
function works
just like the hbam()
function, except the arguments for
rstan::sampling()
do not apply. To fit the FBAM_MINI model
using default settings, we would run:
Models of the kind included in the hbamr package face an inevitable tradeoff between nuance (i.e. model complexity) and execution times. For small data sets (like ANES 1980), all models provide reasonable running times, but for large data sets (like ANES 2012), more complex models like the standard HBAM model tend to get slow. In these cases, the simpler HBAM_MINI model may be a useful alternative. For very large data sets, fitting the FBAM_MINI model via optimization may be the best alternative.
Users relying on MCMC should consider reducing the number of
posterior draws. For most applications, 2000 draws per chain (setting
iter = 2000
with thin = 1
) may be sufficient.
This may result in warnings about low accuracy in the tails of some
distributions, but whether higher accuracy is required will depend on
the goal of the analysis and should be considered on a casebycase
basis.
The table below shows execution times for a set of relevant scenarios
(not counting postprocessing). The warmup
argument was
left at 1000 in all of the tests using MCMC, except the one with 1000
iterations, where it was set to 500.
HBAM iter = 4000 
HBAM_MINI iter = 4000 
HBAM_MINI iter = 2000 
HBAM_MINI iter = 1000 
FBAM_MINI optimization 


ANES 1980 (N = 643)  6.5m  3.5m  2m  1.5m  2s 
ANES 2012 (N = 4949)  3h 20m  1h 15m  45m  30m  15s 
The hbamr package uses ggplot2 for
plotting (which means ggplotthemes can be added to the plots). The
function plot_stimuli()
plots the marginal posterior
distributions of all stimuli in the data. By default, it will fill the
distributions with shades from blue to red depending on the position on
the scale. The argument rev_color = TRUE
will reverse the
order of the colors.
In this example, we see that John B. Anderson – the former Republican who ran as an independent candidate – gets a wider posterior distribution, suggesting that voters were more uncertain about where to place him relative to the others.
The function plot_respondents()
plots the distribution
of estimated respondent positions. It illustrates the uncertainty of the
estimates by calculating the population density for each of a set of
posterior draws. The default is to use 15 draws for each respondent, but
this can be altered by specifying the argument n_draws
. The
plot_respondents()
function also plots the estimated
stimulus positions by default, but this behavior can be turned off by
adding the argument inc_stimuli = FALSE
.
Users who want to customize the plots further can obtain the
underlying data by using the function get_plot_data()
. This
function accepts the same n_draws
argument as
plot_respondents()
. The output is a list of three tibbles:
The first element contains the posterior mean stimulus positions, as
well as the \(x\) and \(y\)values of the posterior modes (which
can be useful for labeling the distributions). The second element
contains the posterior draws for the stimulus positions (which can be
used to calculate marginal posterior densities). The third element
contains the selected number of posterior draws for each respondent
(which form the key ingredient for the plot_respondents()
function).
The function plot_over_self()
plots the distributions of
key parameter estimates over the respondents’ selfplacements. In
addition to a stanfit
object produced by
hbam()
, this function requires the list of data produced by
prep_data()
. The function also takes the argument
par
, which can be either of the following:
"alpha"
, "beta"
, "abs_beta"
,
"lambda"
, "chi"
, and "eta"
.
"abs_beta"
calls for the absolute value of beta to be used.
By default, the function uses posterior median estimates, but this can
be changed by specifying estimate = "mean"
.
This function can, for instance, be used to assess the accuracy of
respondents’ answers. In particular, when the argument
par = "eta"
is specified, the plotting function will
display \(\sqrt{\eta_i} / J\), which
equals the average error for each individual (the mean of \(\sigma_{ij}\) for each \(i\) across \(j\)). The point estimates will still be
calculated using the posterior median, unless the argument
estimate = "mean"
is added. (This plot is not available for
the homoskedastic variants of the model (such as HBAM_MINI) as these do
not estimate individual \(\eta\)
parameters.)
The plot_over_self()
function may also be used to
compare results from several models by supplying the model fits as a
list. To compare the distributions of estimated shift parameters from
the HBAM and HBAM_MINI models, we would run:
For these models, the draws for \(\beta\) combine the separate parameters for each flippingstate. The absolute value of \(\beta\) may therefore be better suited for examining the extent to which each individual stretches the ideological space. To inspect the distribution of these values across selfplacements, we would run:
The pattern above, where respondents with more extreme selfplacements have more extreme \(\beta\) parameters, is exactly the kind of differential item functioning that the models in this package are intended to correct for: These respondents tend to place both stimuli and themselves further out on the scale than others do, thus appearing more extreme in comparison.
To see whether these \(\beta\) parameters are likely to be positive or negative, we can look at the expectation of the flipping parameters. These represent each respondent’s probability of not flipping the scale:
In this example, flipping is very uncommon, but respondents who place themselves in the middle have a somewhat higher flippingprobability. This may suggest that some of these respondents are less informed about politics and provide less accurate answers.
It may also be useful to inspect the distribution of the scaled respondent positions over the selfplacements. This illustrates the extent to which the model has transformed the original data. In this example, the impact of the models is generally modest, although a few respondents have been detected as having flipped the scale, and thus have had their selfplacement flipped back.
The package also contains a wrapper for rstan::summary()
called get_est()
. This function takes the arguments
object
– a stanfit
object produced by
hbam()
or a list produced by fbam()
– and
par
– the name of the parameter(s) to be summarized. The
function returns a tibble, which by default contains the posterior mean,
the 95% credible interval, the posterior median, the estimated number of
effective draws, and the split Rhat. One can obtain other posterior
quantiles by using the argument probs
. To get a 50%
credible interval (and no median), one would add the argument
probs = c(0.25, 0.75)
. To include the Monte Carlo standard
error and the posterior standard deviation, use the argument
simplify = FALSE
. (When applied to outputs from
fbam()
, get_est()
only returns point
estimates.)
The posterior draws for the stimulus positions can be summarized as follows:
## # A tibble: 6 × 6
## mean `2.5%` `50%` `97.5%` n_eff Rhat
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.02 1.10 1.02 0.941 2160. 1.00
## 2 1.62 1.55 1.62 1.68 2013. 1.00
## 3 1.68 1.76 1.68 1.60 2473. 1.00
## 4 0.453 0.574 0.453 0.333 3159. 1.00
## 5 1.42 1.36 1.42 1.48 1912. 1.00
## 6 1.19 1.25 1.19 1.13 2404. 0.999
The equivalent call for the respondent positions is:
## # A tibble: 643 × 6
## mean `2.5%` `50%` `97.5%` n_eff Rhat
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.965 1.96 1.27 2.62 2862. 1.00
## 2 1.21 0.613 1.26 2.57 3421. 1.00
## 3 1.39 0.121 1.44 2.35 3212. 1.00
## 4 0.0128 1.80 0.00469 1.76 3745. 1.00
## 5 0.197 2.04 0.253 1.89 4089. 1.00
## 6 1.45 1.78 1.68 2.94 2336. 1.00
## 7 0.157 1.15 0.159 0.857 2975. 1.00
## 8 0.943 0.738 0.991 2.01 2924. 1.00
## 9 0.587 0.857 0.623 1.73 3691. 1.00
## 10 0.125 1.02 0.136 1.14 3654. 1.00
## # ℹ 633 more rows
The rstan::sampling()
function that hbam()
uses automatically performs a number of key diagnostic checks after
sampling and issues warnings when a potential issue is detected. The
authors of rstan emphasize diagnostics and careful
model development, and users of rstan will more
frequently encounter warnings than users of rjags. One
warning users of this package may encounter is that the Bulk or Tail
Effective Sample Size (ESS) is too low (see https://mcstan.org/misc/warnings.html). The most
straightforward solution to this issue is to increase the number of
posterior draws, using the iter
argument. However, this
increases the computational load, and users should consider carefully
what level of accuracy they need.
Because the hbam()
function returns a
stanfit
object, the model fit can be examined using the
full range of diagnostic tools from the rstan package.
Users should consult the rstan documentation for
details on the various diagnostic tests and plots that are available.
One example of the available tools is traceplot()
:
A useful way to compare alternative models is to estimate their outofsample prediction accuracy. More specifically, we can estimate their expected log pointwise predictive density for a new data set (ELPD). It is most straightforward to do so for the stimuli placements, and this is informative because these form the basis for estimating most individual level parameters. A similar analysis for the selfplacement data is generally not possible, as the models require these placements to estimate the corresponding latent respondent positions. This section therefore focuses on estimating the ELPD for stimulus placements.
The rstan and loo packages contain functions for estimating ELPDs by performing approximate leaveoneout (LOO) crossvalidation using Pareto smoothed importance sampling (PSIS). However, PSISLOO only works when all Pareto k values are sufficiently low, or when the number of high values is so low that moment matching can be used on the problematic cases. In practice, this is not always the case, and it rarely seems to be the case with the BAM model.
The hbamr package therefore includes model codes and
functions to perform Kfold crossvalidation. The function
hbam_cv()
is similar to hbam()
and takes the
same arguments, but will perform a Kfold crossvalidation for
the chosen model. In contrast to hbam()
, the default for
hbam_cv()
is to not allow respondents to have any missing
values (allow_miss = 0
). The reason is that the
crossvalidation essentially creates missing values by excluding some
data from each run.
A key choice when performing crossvalidation is to set K (the number of folds to use), and the default in this package is 10. The function is parallelized so that users can specify a higher number of cores than chains and run chains for different folds simultaneously. To preserve memory, the function extracts the summaries of the loglikelihoods for the heldout data and drops the stanfit objects once this is done. The memory requirements of the function is therefore similar to running a single analysis with one chain per core. The function that splits the data into K folds uses a default seed to produce the same folds each time, unless a different seed is specified.
To perform 10fold crossvalidation for a selection of models, we could run:
elpd_hbam < hbam_cv(self, stimuli, model = "HBAM")
elpd_hbam_2 < hbam_cv(self, stimuli, model = "HBAM_2")
elpd_hbam_NE < hbam_cv(self, stimuli, model = "HBAM_NE")
elpd_hbam_0 < hbam_cv(self, stimuli, model = "HBAM_0")
elpd_bam < hbam_cv(self, stimuli, model = "BAM")
Rather than returning stanfit objects, the function returns a data frame containing the estimated ELPD and its standard error. To compare results across models, we could run:
elpds < rbind(elpd_bam, elpd_hbam, elpd_hbam0, elpd_hbam_ne, elpd_hbam_2)
elpds[order(elpds$ELPD), ]
## ELPD SE
## BAM 5835.7 58.6
## HBAM_0 5766.9 51.2
## HBAM 5525.1 48.2
## HBAM_NE 5524.6 48.6
## HBAM_2 5521.2 48.3
We see that the unpooled BAM model is worse at predicting outofsample data than the other models, suggesting it overfits the data. The HBAM_0 model – which does not allow for scale flipping – also performs worse, suggesting it is too restrictive and underfits. The other three models perform equally well, as the differences are too small to be interpreted substantively. Their equal performance is not very surprising, as the models are very similar, but it should be noted that HBAM_2 might perform better or worse than the other models on a different data set.
We could also perform crossvalidation for the models that do not account for heteroskedastic errors:
elpd_hbam_HM < hbam_cv(self, stimuli, model = "HBAM_HM")
elpd_hbam_MINI < hbam_cv(self, stimuli, model = "HBAM_MINI")
rbind(elpd_hbam_hm, elpd_hbam_mini)
## ELPD SE
## HBAM_HM 5885.3 47.1
## HBAM_MINI 5889.4 47.1
The estimated ELPDs are notably higher for these two models compared to their respective heteroskedastic counterparts. This suggest there is a considerable degree of heteroskedasticity in the data – which is not surprising.
Modeling the heteroskedasticity increases prediction accuracy, but it should be noted that this does not necessarily translate into much more accurate estimates of key model outputs. As shown in section on plotting, the results for the HBAM and HBAM_MINI models are very similar. In fact, their estimated respondent positions correlate at .97 and .99, depending on whether we use the posterior means or medians. Their estimated stimulus positions also correlate at .99, but this masks the fact that the HBAM_MINI model places the less well known candidate John B. Anderson further to the left than the HBAM model does. This illustrates that there are some subtle differences that users should be aware of, even if these models tend to produce very similar results.
The functions in this package accept a seed argument for all operations that involve a random number generator. Supplying a seed is sufficient to get the exact same results in repeated runs on the same system, but it does not ensure exact replication across systems or software versions. The Stan Reference Manual explains why:
Floating point operations on modern computers are notoriously difficult to replicate because the fundamental arithmetic operations, right down to the IEEE 754 encoding level, are not fully specified. The primary problem is that the precision of operations varies across different hardware platforms and software implementations.
Stan is designed to allow full reproducibility. However, this is only possible up to the external constraints imposed by floating point arithmetic.
In short, running the functions in this package on different systems will likely yield slightly different results. It should be noted, however, that the differences across systems will be minimal and substantively negligible as long as the user obtains a sufficient number of posterior draws.