Proto_emulator {hmer}

Prototype Class for Emulator-like Objects

Description

Converts a prediction object into a form useable in hmer.

The history matching process can be used for objects that are not created by the hmer package: most notably Gaussian Process (GP) emulators but even for simple linear models. This R6 class converts such an object into a form that can be called directly and reliably by the methods of the package, including for visualisation and diagnostics.

Constructor

Proto_emulator$new(ranges, output_name, predict_func, variance_func, ...)

Arguments

Required:

ranges A list of ranges for the inputs to the model.

output_name The name of the output modelled.

predict_func The function that provides the predictions at a new point or points. The first argument of this function should be x, where x is a data.frame of points. Additional arguments can be specified as long as they match additional objects passed via ... (see below for details).

variance_func The function that encodes the prediction error due to the model of choice. This, too, takes an argument x as above as its first argument. Additional arguments can be specified as long as they match additional objects passed via ... (see below for details).

Optional:

implausibility_func A function that takes points x and a target z (and potentially a cutoff value cutoff and additional arguments) and returns a measure of closeness of the predicted value to the target (or a boolean representing whether the prediction is within the specified cutoff). Any custom implausibility should satisfy the definition: that is, a point that is unlikely to match to the observation should have higher implausibility than a point likely to match to the observation. If, for example, a likelihood to be maximised is used as a surrogate for an implausibility function, then one should transform it accordingly.

If this argument is not provided, the standard implausibility is used: namely, the absolute value of the difference between prediction and observation, divided by the square root of the sum in quadrature of the errors.

Additional arguments can be specified as long as they match additional objects passed via ... (see below for details).

print_func If the prediction object has a suitable print function that one wishes to transfer to the R6 class (e.g. summary.lm), it is specified here.

... Additional objects to pass to predict_func, variance_func, implausibility_func or print_func. The names of these objects must match the additional argument names in the corresponding functions.

Constructor Details

The constructor must take, as a minimum, the first four arguments (input ranges, output name, and the prediction and variance functions). Default behaviour exists if the implausibility function and print function are not specified. The output of the constructor is an R6 object with the classes "Emulator" and "EmProto".

Accessor Methods

Note that these have the same external structure as those in Emulator.

get_exp(x) Returns the prediction.

get_cov(x) Returns the prediction error.

implausibility(x, z, cutoff = NULL) Returns the 'implausibility'.

print() Prints relevant details of the object.

Examples

    # Use linear regression with an "error" on the SIR dataset.
    ranges <- list(aSI = c(0.1, 0.8), aIR = c(0, 0.5), aSR = c(0, 0.05))
    targets <- SIREmulators$targets
    lms <- purrr::map(names(targets),
     ~step(lm(data = SIRSample$training[,c(names(ranges), .)],
      formula = as.formula(paste0(., "~(",
       paste0(names(ranges), collapse = "+"),
       ")^2"
      ))
    ), trace = 0))
    # Set up the proto emulators
    proto_ems <- purrr::map(seq_along(lms), function(l) {
      Proto_emulator$new(
         ranges,
         names(targets)[l],
         function(x) predict(lms[[l]], x),
         function(x) predict(lms[[l]], x, se.fit = TRUE)$se.fit^2 +
            predict(lms[[l]], x, se.fit = TRUE)$residual.scale^2,
         print_func = function() print(summary(lms[[l]]))
      )
    }) |> setNames(names(targets))
    # Test with some hmer functions
    nth_implausible(proto_ems, SIRSample$validation, targets)
    emulator_plot(proto_ems)
    emulator_plot(proto_ems, 'imp', targets = targets)
    validation_diagnostics(proto_ems, targets, SIRSample$validation)
    new_points <- generate_new_design(proto_ems, 100, targets)