```
library(hmer)
library(ggplot2)
set.seed(1)
```

This vignette briefly outlines the main functionality of the
`hmer`

package. It can be seen as a quick reference to the
main functions and usage of the package and will not detail the
mathematical framework of emulation and history matching: for a more
in-depth treatment, see the other vignettes in the package.

We use a set of (included) model run datasets to demonstrate the functionality - all have been generated from an SIRS model using a system of differential equations. The stochastic equivalent (using the Gillespie algorithm) will be dealt with in a later vignette.

The first step, given a dataset (here the `SIRSample`

training set), is to train an emulator to each output. We need to
provide details about the parameters (and their ranges) to the model and
the outputs from the model, as well as the data itself. Then the
function `emulator_from_data(data, outputs, ranges, ...)`

produces a set of `Emulator`

objects, as a named list.

```
<- list(aSI = c(0.1, 0.8), aIR = c(0, 0.5), aSR = c(0, 0.05))
input_ranges <- c('nS', 'nI', 'nR')
output_names <- emulator_from_data(SIRSample$training, output_names, input_ranges)
wave1_emulators #> Fitting regression surfaces...
#> Building correlation structures...
#> Creating emulators...
#> Performing Bayes linear adjustment...
```

An emulator is an R6 object, with the requisite print statement giving the core details of the emulator. It also contains a Correlator object with similar R6 properties.

```
$nS
wave1_emulators#> Parameters and ranges: aSI: c(0.1, 0.8): aIR: c(0, 0.5): aSR: c(0, 0.05)
#> Specifications:
#> Basis functions: (Intercept); aSI; aIR; aSI:aIR
#> Active variables aSI; aIR
#> Regression Surface Expectation: 572.2885; -412.1201; 249.8202; 113.7759
#> Regression surface Variance (eigenvalues): 0; 0; 0; 0
#> Correlation Structure:
#> Bayes-adjusted emulator - prior specifications listed.
#> Variance (Representative): 6472.576
#> Expectation: 0
#> Correlation type: exp_sq
#> Hyperparameters: theta: 0.9033
#> Nugget term: 0.05
#> Mixed covariance: 0 0 0 0
$nS$corr
wave1_emulators#> Correlation type: exp_sq
#> Hyperparameters: theta: 0.9033
#> Nugget term: 0.05
```

The default training process assumes the following:

- The correlation structure is exponential-squared;
- The regression surface is no more than quadratic in the inputs;
- The specifics of regression parameters, correlation length, and overall variance is to be determined during the training process;
- The output emulators are trained using Bayes Linear adjustment before being outputted.

Any of these default behaviours can be modified; for instance, manually setting the correlation lengths requires the provision of a numeric vector of correlation lengths:

```
<- emulator_from_data(SIRSample$training, output_names, input_ranges, c_lengths = c(0.6, 0.7, 0.85))
custom_emulators #> Fitting regression surfaces...
#> Building correlation structures...
#> Creating emulators...
#> Performing Bayes linear adjustment...
$nR$corr
custom_emulators#> Correlation type: exp_sq
#> Hyperparameters: theta: 0.85
#> Nugget term: 0.05
```

One could instead specify a different correlation function, for instance Matérn:

```
<- emulator_from_data(SIRSample$training, output_names, input_ranges, corr_name = "matern")
matern_emulators #> Fitting regression surfaces...
#> Building correlation structures...
#> Creating emulators...
#> Performing Bayes linear adjustment...
```

In any event, having trained emulators it is crucial to ensure that
they are accurately representing the model behaviour, at the very least
over the region of potential interest. The
`validation_diagnostics`

function produces some simple plots
to check this. It requires a separate hold-out or validation set of
model runs. Some of the diagnostic functions also require a set of
targets - values which we expect the model to match to. These can be
specified as a named list, each element consisting of an observation
value `val`

and an associated uncertainty `sigma`

;
alternatively we can provide an upper and lower bound \([a,b]\) corresponding to the target range.
A combination thereof can be provided in a given set of targets.

```
<- list(
targets nS = c(580, 651),
nI = list(val = 169, sigma = 8.45),
nR = c(199, 221)
)
```

`<- validation_diagnostics(wave1_emulators, targets, SIRSample$validation, plt = TRUE) invalid_points `

Emulators can be used as a (statistical) surrogate for the model, and
in particular can be used to investigate parameter behaviour across the
space. The main function for doing so here is
`emulator_plot`

. The expectation, variance, and standard
deviation can be plotted across any two-dimensional slice of the
parameter space. If a list of emulators is provided, all relevant
outputs are plotted; otherwise the single chosen emulator is
outputted.

`emulator_plot(wave1_emulators)`

`emulator_plot(wave1_emulators$nS, params = c('aSI', 'aIR'), fixed_vals = list(aSR = 0.03))`

`emulator_plot(wave1_emulators$nI, plot_type = 'var')`

`emulator_plot(wave1_emulators$nI, plot_type = 'sd')`

Almost all the return values of plots produced by `hmer`

are `ggplot`

objects, and so can be augmented after the
fact.

`emulator_plot(wave1_emulators$nS, params = c('aIR', 'aSI')) + ggplot2::geom_point(data = SIRSample$training, ggplot2::aes(x = aSI, y = aIR))`

Using these, we can plot implausibility across the parameter space,
again using `emulator_plot`

. We can also combine the
implausibilities into a combined measure of point suitability by
considering maximum implausibility across all outputs. While
traditionally, implausibility plots are green-red, a colourblind
friendly palette is available with the use of `cb=TRUE`

.

`emulator_plot(wave1_emulators, plot_type = 'imp', targets = targets)`

`emulator_plot(wave1_emulators, plot_type = 'nimp', targets = targets, cb = TRUE)`

While these plots represent slices of parameter space, other
functionality allows a rough understanding of the behaviour across the
full space.In particular, the `plot_lattice`

function
provides a plot of optical depth and minimum implausibility for all
parameter combinations. Since this is generating a plot that represents
the full space and not a two-dimensional slice, it typically takes a
little longer than the previous plots considered.

```
plot_lattice(wave1_emulators, targets)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#> Warning: Removed 27 rows containing missing values (`geom_smooth()`).
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#> Warning: Removed 25 rows containing missing values (`geom_smooth()`).
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
```

In preparation for proposing new points, we can also look at the
optimal ‘cutoff’, if we wish to remove a certain amount of parameter
space, using the `space_removed`

function.

`space_removed(wave1_emulators, targets, ppd = 15) + geom_vline(xintercept = 3, lty = 2)`

We see, for instance, that choosing a cutoff of 3 (roughly
corresponding to a 3-sigma deviation from the target value) is
sufficient to rule out over 95% of the space - coupled with the fact
that our diagnostics were so healthy suggests that this is a very
reasonable first cut. This is heuristic based on the number of points we
provide to the function: the `ppd`

(points-per-dimension)
argument determines the size of the grid we evaluate over. Here we have
considered \(15^3=3375\) points - if we
wanted to examine the space removed in greater depth we could increase
the value of this argument. A similar argument exists for
`plot_lattice`

.

Eventually, the aim of history matching is to propose new points
which can enter into the model, the results of which can train new
emulators, repeating the cycle; the `generate_new_runs`

function does just that. There are multiple options that can be passed
to this function: the default behaviour should be adequate for most
situations, with the consideration of the cutoff value mentioned above.
Information about the progress of the point proposal can be suppressed
by setting the argument `verbose = FALSE`

- by default it
will be `TRUE`

if we are stepping through this by hand, but
not if we run this at arm’s length (for example on a cluster).

```
<- generate_new_runs(wave1_emulators, 90, targets)
new_points #> Proposing from LHS...
#> LHS has high yield - no other methods required.
#> Proposing from LHS...
#> Selecting final points using maximin criterion...
plot(rbind(rbind(SIRSample$training, SIRSample$validation)[,names(input_ranges)], new_points), pch = 16, cex = 0.8, col = rep(c('black', 'blue'), each = 90))
```

The plot command above is replicated in the `hmer`

package
itself, to a degree. We first create a list of ‘waves’; each element of
the list is a data.frame containing all points proposed at that time.
For now, we merely look at the proposed points and not the resulting
model values.

`<- list(SIRSample$training[,c('aSI', 'aIR', 'aSR')], new_points) wave.points `

There are a number of explanatory plots that can be produced, given a
set of wave data. Some require the model to have been run on the points,
but one that doesn’t is `wave_points`

:

`wave_points(wave.points, c('aSI', 'aIR', 'aSR'))`

This shows the evolution of the point proposals as the waves go on.
In each lower-diagonal plot, we have a plot of the proposed points with
respect to two of the parameters; in the diagonal is a density plot for
each parameter. This allows us to consider the shape of the
non-implausible space as we proceed through the history match. Related
functions, with similar output, are `wave_values`

and
`wave_dependencies`

, both of which we detail in the next
section.

As mentioned above, functions exist to visualise the evolution of the
non-implausible space and the points proposed. By definition such plots
require us to have performed multiple waves of history matching - rather
than repeat the commands above we will use the
`SIRMultiWaveData`

dataset provided. This contains a list of
four waves of points from this model (including wave 0), along with the
model evaluations. We can use this to look at dependencies of the
outputs on the inputs (`wave_dependencies`

), the evolution of
the output values obtained (`wave_values`

), and the overall
behaviour of the model runs (`simulator_plot`

).

`wave_values(SIRMultiWaveData, targets)`

`wave_dependencies(SIRMultiWaveData, targets)`

`simulator_plot(SIRMultiWaveData, targets, barcol = 'white')`

All of these plots, as well as `wave_points`

, are wrapped
into a single function, `diagnostic_wrap`

. This will also
produce log-transformed and normalised versions of the simulator plot,
for situations where the outputs have vastly different magnitudes (for
example, when matching to a proportion of infected people as well as raw
case numbers).

Each of the functions in constructing emulators has a number of
options with which the default behaviour can be modified. However, if
investigation of the emulator structure is not required and one simply
wants the next set of proposed points, then the `full_wave`

function allows this. It requires only a set of points to train the
emulators on. If applying this at later waves, the emulators from
previous waves are provided as a list to the `old_emulators`

argument.

```
<- suppressWarnings(full_wave(do.call('rbind.data.frame', SIRSample), list(aSI = c(0.1, 0.8), aIR = c(0, 0.5), aSR = c(0, 0.05)), targets))
f_w #> Training emulators...
#> Fitting regression surfaces...
#> Building correlation structures...
#> Creating emulators...
#> Performing Bayes linear adjustment...
#> Performing diagnostics...
#> Generating new points...
```

This provides two things: a next set of points to evaluate the model on, and the emulators that gave rise to them. One should exercise caution when using this - while the function does what it can to ensure that emulators trained and points proposed are done in the most reasonable way, it is no substitute for expert judgement and consideration. Nevertheless, it can be a useful tool for a ‘first pass’ at emulating a system.