Package 'hubEnsembles'

Title: Ensemble Methods for Combining Hub Model Outputs
Description: Functions for combining model outputs (e.g. predictions or estimates) from multiple models into an aggregated ensemble model output.
Authors: Evan L Ray [aut], Li Shandross [aut, cre] , Emily Howerton [aut] , Anna Krystalli [ctb] , Zhian N. Kamvar [ctb] , Nicholas G. Reich [ctb] , Consortium of Infectious Disease Modeling Hubs [cph]
Maintainer: Li Shandross <[email protected]>
License: MIT + file LICENSE
Version: 0.1.9
Built: 2024-12-02 05:44:58 UTC
Source: https://github.com/hubverse-org/hubEnsembles

Help Index


Example model output data for linear_pool()

Description

Toy model output data formatted according to hubverse standards to be used in the examples for linear_pool(). The predictions included are taken from three normal distributions with means -3, 0, 3 and all standard deviations 1.

Usage

component_outputs

Format

component_outputs

A data frame with 123 rows and 5 columns:

model_id

model ID

target

forecast target

output_type

type of forecast

output_type_id

output type ID

value

forecast value


Example weights data for simple_ensemble()

Description

Toy weights data formatted according to hubverse standards to be used in the examples for simple_ensemble()

Usage

fweights

Format

fweights

A data frame with 8 rows and 3 columns:

model_id

model ID

location

FIPS codes

weight

weight


Compute ensemble model outputs as a linear pool, otherwise known as a distributional mixture, of component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, quantile, cdf, and pmf.

Description

Compute ensemble model outputs as a linear pool, otherwise known as a distributional mixture, of component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, quantile, cdf, and pmf.

Usage

linear_pool(
  model_out_tbl,
  weights = NULL,
  weights_col_name = "weight",
  model_id = "hub-ensemble",
  task_id_cols = NULL,
  n_samples = 10000,
  ...
)

Arguments

model_out_tbl

an object of class model_out_tbl with component model outputs (e.g., predictions).

weights

an optional data.frame with component model weights. If provided, it should have a column named model_id and a column containing model weights. Optionally, it may contain additional columns corresponding to task id variables, output_type, or output_type_id, if weights are specific to values of those variables. The default is NULL, in which case an equally-weighted ensemble is calculated. Should be prevalidated.

weights_col_name

character string naming the column in weights with model weights. Defaults to "weight"

model_id

character string with the identifier to use for the ensemble model.

task_id_cols

character vector with names of columns in model_out_tbl that specify modeling tasks. Defaults to NULL, in which case all columns in model_out_tbl other than "model_id", "output_type", "output_type_id", and "value" are used as task ids.

n_samples

numeric that specifies the number of samples to use when calculating quantiles from an estimated quantile function. Defaults to 1e4.

...

parameters that are passed to distfromq::make_q_fn, specifying details of how to estimate a quantile function from provided quantile levels and quantile values for output_type "quantile".

Details

The underlying mechanism for the computations varies for different output_types. When the output_type is cdf, pmf, or mean, this function simply calls simple_ensemble to calculate a (weighted) mean of the component model outputs. This is the definitional calculation for the CDF or PMF of a linear pool. For the mean output type, this is justified by the fact that the (weighted) mean of the linear pool is the (weighted) mean of the means of the component distributions.

When the output_type is quantile, we obtain the quantiles of a linear pool in three steps:

  1. Interpolate and extrapolate from the provided quantiles for each component model to obtain an estimate of the CDF of that distribution.

  2. Draw samples from the distribution for each component model. To reduce Monte Carlo variability, we use quasi-random samples corresponding to quantiles of the estimated distribution.

  3. Collect the samples from all component models and extract the desired quantiles.

Steps 1 and 2 in this process are performed by distfromq::make_q_fn.

Value

a model_out_tbl object of ensemble predictions. Note that any additional columns in the input model_out_tbl are dropped.

Examples

# We illustrate the calculation of a linear pool when we have quantiles from the
# component models. We take the components to be normal distributions with
# means -3, 0, and 3, all standard deviations 1, and weights 0.25, 0.5, and 0.25.
data(component_outputs)
data(weights)

expected_quantiles <- seq(from = -5, to = 5, by = 0.25)
lp_from_component_qs <- linear_pool(component_outputs, weights)

head(lp_from_component_qs)
all.equal(lp_from_component_qs$value, expected_quantiles, tolerance = 1e-2,
          check.attributes = FALSE)

Example model output data for simple_ensemble()

Description

Toy model output data formatted according to hubverse standards to be used in the examples for simple_ensemble()

Usage

model_outputs

Format

model_outputs

A data frame with 24 rows and 8 columns:

model_id

model ID

location

FIPS codes

horizon

forecast horizon

target

forecast target

target_date

date that the forecast is for

output_type

type of forecast

output_type_id

output type ID

value

forecast value


Compute ensemble model outputs by summarizing component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, median, quantile, cdf, and pmf.

Description

Compute ensemble model outputs by summarizing component model outputs for each combination of model task, output type, and output type id. Supported output types include mean, median, quantile, cdf, and pmf.

Usage

simple_ensemble(
  model_out_tbl,
  weights = NULL,
  weights_col_name = "weight",
  agg_fun = mean,
  agg_args = list(),
  model_id = "hub-ensemble",
  task_id_cols = NULL
)

Arguments

model_out_tbl

an object of class model_out_tbl with component model outputs (e.g., predictions).

weights

an optional data.frame with component model weights. If provided, it should have a column named model_id and a column containing model weights. Optionally, it may contain additional columns corresponding to task id variables, output_type, or output_type_id, if weights are specific to values of those variables. The default is NULL, in which case an equally-weighted ensemble is calculated. Should be prevalidated.

weights_col_name

character string naming the column in weights with model weights. Defaults to "weight"

agg_fun

a function or character string name of a function to use for aggregating component model outputs into the ensemble outputs. See the details for more information.

agg_args

a named list of any additional arguments that will be passed to agg_fun.

model_id

character string with the identifier to use for the ensemble model.

task_id_cols

character vector with names of columns in model_out_tbl that specify modeling tasks. Defaults to NULL, in which case all columns in model_out_tbl other than "model_id", "output_type", "output_type_id", and "value" are used as task ids.

Details

The default for agg_fun is "mean", in which case the ensemble's output is the average of the component model outputs within each group defined by a combination of values in the task id columns, output type, and output type id. The provided agg_fun should have an argument x for the vector of numeric values to summarize, and for weighted methods, an argument w with a numeric vector of weights. If it desired to use an aggregation function that does not accept these arguments, a wrapper would need to be written. For weighted methods, agg_fun = "mean" and agg_fun = "median" are translated to use matrixStats::weightedMean and matrixStats::weightedMedian respectively. For matrixStats::weightedMedian, the argument interpolate is automatically set to FALSE to circumvent a calculation issue that results in invalid distributions.

Value

a model_out_tbl object of ensemble predictions. Note that any additional columns in the input model_out_tbl are dropped.

Examples

# Calculate a weighted median in two ways
data(model_outputs)
data(fweights)

weighted_median1 <- simple_ensemble(model_outputs, weights = fweights,
                                    agg_fun = stats::median)
weighted_median2 <- simple_ensemble(model_outputs, weights = fweights,
                                     agg_fun = matrixStats::weightedMedian)
all.equal(weighted_median1, weighted_median2)

Example weights data for linear_pool()

Description

Toy weights data formatted according to hubverse standards to be used in the examples for linear_pool(). Weights are 0.25, 0.5, 0.25.

Usage

weights

Format

weights

A data frame with 3 rows and 2 columns:

model_id

model ID

location

FIPS codes

weight

weight