Title: | Forecast Cases Using Reproduction Numbers |
---|---|
Description: | To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules. |
Authors: | Sam Abbott [aut, cre] |
Maintainer: | Sam Abbott <[email protected]> |
License: | MIT + file LICENSE |
Version: | 0.3.1 |
Built: | 2024-08-24 03:31:12 UTC |
Source: | https://github.com/epiforecasts/EpiSoon |
Allows users to specify a model using the brms::bf()
wrapper from brms
Note that brms
and tidybayes
must both be installed for this
model wrapper to be functional.
brms_model( y = NULL, samples = NULL, horizon = NULL, model = NULL, n_cores = 1, n_chains = 4, n_iter = 2000, ... )
brms_model( y = NULL, samples = NULL, horizon = NULL, model = NULL, n_cores = 1, n_chains = 4, n_iter = 2000, ... )
y |
Numeric vector of time points to forecast |
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
model |
A |
n_cores |
Numeric, the number of cores to use, default of 1 |
n_chains |
Numeric, the number of chains to use, default of 4 |
n_iter |
Numeric, the number of iterations in the sampler to use, default of 4000 |
... |
additional arguments passed to |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: ## Used on its own ## Note: More iterations and chains should be used library(brms) brms_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = brms::bf(y ~ gp(time)), samples = 10, horizon = 7, n_iter = 40, n_chains = 1, refresh = 0 ) ## Used for forecasting ## Note that the timeout parameter has been increased to allow ## for the time for the code to be compiled ## Note: More iterations and chains should be used forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { brms_model(model = brms::bf(y ~ gp(time)), n_iter = 40, n_chains = 1, ...) }, horizon = 7, samples = 10, timeout = 300 ) ## End(Not run)
## Not run: ## Used on its own ## Note: More iterations and chains should be used library(brms) brms_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = brms::bf(y ~ gp(time)), samples = 10, horizon = 7, n_iter = 40, n_chains = 1, refresh = 0 ) ## Used for forecasting ## Note that the timeout parameter has been increased to allow ## for the time for the code to be compiled ## Note: More iterations and chains should be used forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { brms_model(model = brms::bf(y ~ gp(time)), n_iter = 40, n_chains = 1, ...) }, horizon = 7, samples = 10, timeout = 300 ) ## End(Not run)
bsts Model Wrapper
bsts_model(y = NULL, samples = NULL, horizon = NULL, model = NULL)
bsts_model(y = NULL, samples = NULL, horizon = NULL, model = NULL)
y |
Numeric vector of time points to forecast |
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
model |
A |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: library(bsts) ## Used on its own bsts_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 2) }, samples = 10, horizon = 7 ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, horizon = 7, samples = 10 ) ## End(Not run)
## Not run: library(bsts) ## Used on its own bsts_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 2) }, samples = 10, horizon = 7 ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, horizon = 7, samples = 10 ) ## End(Not run)
Compare forecasting models
compare_models( obs_rts = NULL, obs_cases = NULL, models = NULL, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 30, serial_interval = NULL, min_points = 3, rdist = NULL, return_raw = FALSE )
compare_models( obs_rts = NULL, obs_cases = NULL, models = NULL, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 30, serial_interval = NULL, min_points = 3, rdist = NULL, return_raw = FALSE )
obs_rts |
Dataframe of Rt observations to forecast with and score
against. Should contain a |
obs_cases |
Dataframe of case observations to use for case prediction and
scoring. Should contain a |
models |
A list of models. A configuration is given in the
examples. Each model needs to be wrapped in a function that takes a |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
bound_rt |
Logical, defaults to |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
min_points |
Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast. |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
return_raw |
Logical, should raw cases and rt forecasts be returned. Defaults to |
A list of dataframes as produced by evaluate model
but with an additional model column.
## Not run: ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, "ARIMA" = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) } ) ## Compare models evaluations <- compare_models(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Example evaluation plot for comparing forecasts ## with actuals for a range of models and time horizons. plot_forecast_evaluation(evaluations$forecast_rts, EpiSoon::example_obs_rts, c(1, 3, 7)) + ggplot2::facet_grid(model ~ horizon) + cowplot::panel_border() ## Hack to plot observed cases vs predicted plot_forecast_evaluation( evaluations$forecast_cases, EpiSoon::example_obs_cases, c(1, 3, 7) ) + ggplot2::facet_wrap(model ~ horizon, scales = "free") + cowplot::panel_border() ## End(Not run)
## Not run: ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, "ARIMA" = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) } ) ## Compare models evaluations <- compare_models(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Example evaluation plot for comparing forecasts ## with actuals for a range of models and time horizons. plot_forecast_evaluation(evaluations$forecast_rts, EpiSoon::example_obs_rts, c(1, 3, 7)) + ggplot2::facet_grid(model ~ horizon) + cowplot::panel_border() ## Hack to plot observed cases vs predicted plot_forecast_evaluation( evaluations$forecast_cases, EpiSoon::example_obs_cases, c(1, 3, 7) ) + ggplot2::facet_wrap(model ~ horizon, scales = "free") + cowplot::panel_border() ## End(Not run)
Compare timeseries and forecast models
compare_timeseries( obs_rts = NULL, obs_cases = NULL, models = NULL, horizon = 7, samples = 1000, bound_rt = TRUE, min_points = 3, timeout = 30, serial_interval = NULL, rdist = NULL, return_raw = FALSE )
compare_timeseries( obs_rts = NULL, obs_cases = NULL, models = NULL, horizon = 7, samples = 1000, bound_rt = TRUE, min_points = 3, timeout = 30, serial_interval = NULL, rdist = NULL, return_raw = FALSE )
obs_rts |
A dataframe of observed Rts including a |
obs_cases |
A dataframe of observed cases including a |
models |
A list of models. A configuration is given in the
examples. Each model needs to be wrapped in a function that takes a |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
bound_rt |
Logical, defaults to |
min_points |
Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast. |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
return_raw |
Logical, should raw cases and rt forecasts be returned. Defaults to |
A list of dataframes as produced by evaluate model
but with an additional model column.
## Not run: ## Example data obs_rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) obs_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, "ARIMA" = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) } ) ## Compare models evaluations <- compare_timeseries(obs_rts, obs_cases, models, horizon = 7, samples = 10, serial_interval = EpiSoon::example_serial_interval ) evaluations ## Example evaluation plot for comparing forecasts ## with actuals for a range of models and timeseries. plot_forecast_evaluation(evaluations$forecast_rts, obs_rts, c(7)) + ggplot2::facet_grid(model ~ timeseries) + cowplot::panel_border() ## Hack to plot observed cases vs predicted plot_forecast_evaluation( evaluations$forecast_cases, obs_cases, c(7) ) + ggplot2::facet_grid(model ~ timeseries, scales = "free") + cowplot::panel_border() ## End(Not run)
## Not run: ## Example data obs_rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) obs_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, "ARIMA" = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) } ) ## Compare models evaluations <- compare_timeseries(obs_rts, obs_cases, models, horizon = 7, samples = 10, serial_interval = EpiSoon::example_serial_interval ) evaluations ## Example evaluation plot for comparing forecasts ## with actuals for a range of models and timeseries. plot_forecast_evaluation(evaluations$forecast_rts, obs_rts, c(7)) + ggplot2::facet_grid(model ~ timeseries) + cowplot::panel_border() ## Hack to plot observed cases vs predicted plot_forecast_evaluation( evaluations$forecast_cases, obs_cases, c(7) ) + ggplot2::facet_grid(model ~ timeseries, scales = "free") + cowplot::panel_border() ## End(Not run)
Draw from the Serial Interval Probability Distribution
draw_from_si_prob(days_ago = NULL, serial_interval = NULL)
draw_from_si_prob(days_ago = NULL, serial_interval = NULL)
days_ago |
Numeric vector of days in the past. Defaults to |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
A draw from the probability distribution the serial interval.
## Draw draw_from_si_prob(rev(c(1, 2, 4, 10, 1:100)), EpiSoon::example_serial_interval)
## Draw draw_from_si_prob(rev(c(1, 2, 4, 10, 1:100)), EpiSoon::example_serial_interval)
Evaluate a Model for Forecasting Rts
evaluate_model( obs_rts = NULL, obs_cases = NULL, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3, serial_interval = NULL, rdist = NULL, return_raw = FALSE )
evaluate_model( obs_rts = NULL, obs_cases = NULL, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3, serial_interval = NULL, rdist = NULL, return_raw = FALSE )
obs_rts |
Dataframe of Rt observations to forecast with and score
against. Should contain a |
obs_cases |
Dataframe of case observations to use for case prediction and
scoring. Should contain a |
model |
A model object in the format of |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
bound_rt |
Logical, defaults to |
min_points |
Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast. |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
return_raw |
Logical, should raw cases and rt forecasts be returned. Defaults to |
a list of tibbles containing the predicted Rt values (forecast_rts
),
their scores (rt_scores
), as well as predicted cases (forecast_cases
) and
their scores (case_scores
).
## Not run: ## Evaluate a model based on a single sample of input cases evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Samples of observed data sampled_obs <- EpiSoon::example_obs_rts %>% dplyr::mutate(sample = 1) %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(sample = 2)) sampled_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(sample = 1) %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(sample = 2)) ## Evaluate a model across samples evaluate_model(sampled_obs, sampled_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
## Not run: ## Evaluate a model based on a single sample of input cases evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Samples of observed data sampled_obs <- EpiSoon::example_obs_rts %>% dplyr::mutate(sample = 1) %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(sample = 2)) sampled_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(sample = 1) %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(sample = 2)) ## Evaluate a model across samples evaluate_model(sampled_obs, sampled_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
An example data frame of observed cases
example_obs_cases
example_obs_cases
A data frame containing cases reported on each date.
An example data frame of observed Reproduction numbers
example_obs_rts
example_obs_rts
A data frame containing Rts estimated for each date.
An example serial interval probability vector
example_serial_interval
example_serial_interval
A vector giviing the probability for each day
Provides an interface for models from the fable
package.
Note the feasts::ARIMA
model requires the feast
package. If future
is being used fable
will require future.apply
in
order to not silently fail.
fable_model(y = NULL, samples = NULL, horizon = NULL, model = NULL)
fable_model(y = NULL, samples = NULL, horizon = NULL, model = NULL)
y |
Numeric vector of time points to forecast |
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
model |
A |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: ## Used on its own fable_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = fable::ARIMA(y ~ time), samples = 10, horizon = 7 ) forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) }, horizon = 7, samples = 10 ) ## End(Not run)
## Not run: ## Used on its own fable_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = fable::ARIMA(y ~ time), samples = 10, horizon = 7 ) forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { fable_model(model = fable::ARIMA(y ~ time), ...) }, horizon = 7, samples = 10 ) ## End(Not run)
Forecasts Cases for a Rt Forecasts
forecast_cases( cases = NULL, fit_samples = NULL, serial_interval = NULL, forecast_date = NULL, horizon = NULL, rdist = NULL )
forecast_cases( cases = NULL, fit_samples = NULL, serial_interval = NULL, forecast_date = NULL, horizon = NULL, rdist = NULL )
cases |
A dataframe containing |
fit_samples |
A dataframe as produced by |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
forecast_date |
A character string date (format "yyyy-mm-dd") indicating
the forecast date. Defaults to |
horizon |
Numeric, the time horizon over which to predict. |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
Forecast cases for over a future forecast horizon
## Not run: ## Rt forecast forecast <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) forecast_cases(EpiSoon::example_obs_cases, fit_samples = forecast, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
## Not run: ## Rt forecast forecast <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) forecast_cases(EpiSoon::example_obs_cases, fit_samples = forecast, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
Forecasts Cases Directly
forecast_cases_directly( cases = NULL, model, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 100 )
forecast_cases_directly( cases = NULL, model, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 100 )
cases |
A dataframe containing |
model |
A model object in the format of |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
bound_rt |
Logical, defaults to |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
Forecast cases over a future forecast horizon
## Not run: forecast_cases_directly(EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) ## End(Not run)
## Not run: forecast_cases_directly(EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) ## End(Not run)
Allows users to forecast using models from the forecast
package.
Note that forecast
must be installed for this model wrapper to be functional.
forecast_model(y = NULL, samples = NULL, horizon = NULL, model = NULL, ...)
forecast_model(y = NULL, samples = NULL, horizon = NULL, model = NULL, ...)
y |
Numeric vector of time points to forecast |
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
model |
A |
... |
pass further arguments to the forecast models |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: ## Used on its own forecast_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = forecast::auto.arima, samples = 10, horizon = 7 ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { forecast_model(model = forecast::ets, ...) }, horizon = 7, samples = 10 ) # run with non-default arguments forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { forecast_model( model = forecast::ets, damped = TRUE, ... ) }, horizon = 7, samples = 10 ) models <- list( "ARIMA" = function(...) { forecast_model(model = forecast::auto.arima, ...) }, "ETS" = function(...) { forecast_model(model = forecast::ets, ...) }, "TBATS" = function(...) { forecast_model(model = forecast::tbats, ...) } ) ## Compare models evaluations <- compare_models(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) plot_forecast_evaluation(evaluations$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) + ggplot2::facet_grid(~model) + cowplot::panel_border() ## End(Not run)
## Not run: ## Used on its own forecast_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, model = forecast::auto.arima, samples = 10, horizon = 7 ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { forecast_model(model = forecast::ets, ...) }, horizon = 7, samples = 10 ) # run with non-default arguments forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { forecast_model( model = forecast::ets, damped = TRUE, ... ) }, horizon = 7, samples = 10 ) models <- list( "ARIMA" = function(...) { forecast_model(model = forecast::auto.arima, ...) }, "ETS" = function(...) { forecast_model(model = forecast::ets, ...) }, "TBATS" = function(...) { forecast_model(model = forecast::tbats, ...) } ) ## Compare models evaluations <- compare_models(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) plot_forecast_evaluation(evaluations$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) + ggplot2::facet_grid(~model) + cowplot::panel_border() ## End(Not run)
Fit and Forecast using a Model
forecast_rt( rts, model, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 100 )
forecast_rt( rts, model, horizon = 7, samples = 1000, bound_rt = TRUE, timeout = 100 )
rts |
A dataframe of containing two variables |
model |
A model object in the format of |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
bound_rt |
Logical, defaults to |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
A dataframe of samples containing the following variables:
sample
, date
, rt
, and horizon
.
## Not run: forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) ## End(Not run)
## Not run: forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAutoAr(ss, y = y, lags = 10) }, ...) }, horizon = 7, samples = 10 ) ## End(Not run)
Allows users to forecast using ensembles from the forecastHybrid
package. Note that
whilst weighted ensembles can be created this is not advised when samples > 1 as currently
samples are derived assuming a normal distribution using the upper and lower confidence intervals of the ensemble.
These confidence intervals are themselves either based on the unweighted mean of the ensembled
models or the maximum/minimum from the candiate models. Note that forecastHybrid
must be installed for this
model wrapper to be functional.
forecastHybrid_model( y = NULL, samples = NULL, horizon = NULL, model_params = NULL, forecast_params = NULL )
forecastHybrid_model( y = NULL, samples = NULL, horizon = NULL, model_params = NULL, forecast_params = NULL )
y |
Numeric vector of time points to forecast |
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
model_params |
List of parameters to pass to |
forecast_params |
List of parameters to pass to |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: library(forecastHybrid) ## Used on its own forecastHybrid_model( y = EpiSoon::example_obs_rts$rt, samples = 10, horizon = 7 ) ## Used with non-default arguments ## Note that with the current sampling from maximal confidence intervals model ## Weighting using cross-validation will only have an impact when 1 sample is used. forecastHybrid_model( y = EpiSoon::example_obs_rts$rt, samples = 1, horizon = 7, model_params = list( cvHorizon = 7, windowSize = 7, rolling = TRUE, models = "zeta" ) ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts, model = EpiSoon::forecastHybrid_model, horizon = 7, samples = 1 ) ## Used for forcasting with non-default arguments forecast_rt(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::forecastHybrid_model( model_params = list(models = "zte"), forecast_params = list(PI.combination = "mean"), ... ) }, horizon = 7, samples = 10 ) ## End(Not run)
## Not run: library(forecastHybrid) ## Used on its own forecastHybrid_model( y = EpiSoon::example_obs_rts$rt, samples = 10, horizon = 7 ) ## Used with non-default arguments ## Note that with the current sampling from maximal confidence intervals model ## Weighting using cross-validation will only have an impact when 1 sample is used. forecastHybrid_model( y = EpiSoon::example_obs_rts$rt, samples = 1, horizon = 7, model_params = list( cvHorizon = 7, windowSize = 7, rolling = TRUE, models = "zeta" ) ) ## Used for forecasting forecast_rt(EpiSoon::example_obs_rts, model = EpiSoon::forecastHybrid_model, horizon = 7, samples = 1 ) ## Used for forcasting with non-default arguments forecast_rt(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::forecastHybrid_model( model_params = list(models = "zte"), forecast_params = list(PI.combination = "mean"), ... ) }, horizon = 7, samples = 10 ) ## End(Not run)
Iteratively Forecast Cases Using an Iterative Rt Forecast
iterative_case_forecast( it_fit_samples = NULL, cases = NULL, serial_interval, rdist = NULL )
iterative_case_forecast( it_fit_samples = NULL, cases = NULL, serial_interval, rdist = NULL )
it_fit_samples |
Dataframe of iterative forecasts as produced by |
cases |
A dataframe containing |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
A dataframe of iterative case forecasts
## Not run: ## Iterative Rt forecast it_forecast <- iterative_rt_forecast(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Iterative case forecast iterative_case_forecast( it_fit_samples = it_forecast, cases = EpiSoon::example_obs_cases, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
## Not run: ## Iterative Rt forecast it_forecast <- iterative_rt_forecast(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Iterative case forecast iterative_case_forecast( it_fit_samples = it_forecast, cases = EpiSoon::example_obs_cases, serial_interval = EpiSoon::example_serial_interval ) ## End(Not run)
Iteratively forecast directly on cases
iterative_direct_case_forecast( cases, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3 )
iterative_direct_case_forecast( cases, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3 )
cases |
A dataframe containing |
model |
A model object in the format of |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
bound_rt |
Logical, defaults to |
min_points |
Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast. |
A tibble of iterative forecasts
## Not run: iterative_direct_case_forecast(EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, min_points = 4 ) ## End(Not run)
## Not run: iterative_direct_case_forecast(EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, min_points = 4 ) ## End(Not run)
Iteratively Forecast
iterative_rt_forecast( rts, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3 )
iterative_rt_forecast( rts, model = NULL, horizon = 7, samples = 1000, timeout = 30, bound_rt = TRUE, min_points = 3 )
rts |
A dataframe of containing two variables |
model |
A model object in the format of |
horizon |
Numeric, the time horizon over which to predict. |
samples |
Numeric, number of samples to take. |
timeout |
Numeric, timeout of model fitting in seconds. Defaults to 30 seconds. |
bound_rt |
Logical, defaults to |
min_points |
Numeric, defaults to 3. The minimum number of time points at which to begin iteratively evaluating the forecast. |
A tibble of iterative forecasts
## Not run: iterative_rt_forecast(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, min_points = 4 ) -> tmp ## End(Not run)
## Not run: iterative_rt_forecast(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10, min_points = 4 ) -> tmp ## End(Not run)
Summary plots to compare timeseries and forecast models
plot_compare_timeseries( compare_timeseries_output, type = c("summary_score", "horizon_score", "region_score"), score = c("Bias", "CRPS", "Dispersion", "AE (median)", "SE (mean)") )
plot_compare_timeseries( compare_timeseries_output, type = c("summary_score", "horizon_score", "region_score"), score = c("Bias", "CRPS", "Dispersion", "AE (median)", "SE (mean)") )
compare_timeseries_output |
A named list of dataframes produced by
|
type |
Type(s) of summary plot to be produced for Rt and case
observations for each model in n |
score |
(Optional) One or more of
|
A named list of ggplot2
objects
## Not run: obs_rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) obs_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) models <- list( "AR 3" = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ...) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ...) } ) forecast_eval <- compare_timeseries(obs_rts, obs_cases, models, horizon = 10, samples = 10, serial_interval = EpiSoon::example_serial_interval ) ## Produce all plots plot_compare_timeseries(forecast_eval) ## Produce subsets of plots plot_compare_timeseries(forecast_eval, type = "summary_score") plot_compare_timeseries(forecast_eval, type = "summary_score", score = "Bias" ) plot_compare_timeseries(forecast_eval, type = "horizon_score") plot_compare_timeseries(forecast_eval, type = "region_score") plot_compare_timeseries(forecast_eval, type = c("horizon_score", "region_score") ) ## End(Not run)
## Not run: obs_rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) obs_cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) models <- list( "AR 3" = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ...) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model(model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ...) } ) forecast_eval <- compare_timeseries(obs_rts, obs_cases, models, horizon = 10, samples = 10, serial_interval = EpiSoon::example_serial_interval ) ## Produce all plots plot_compare_timeseries(forecast_eval) ## Produce subsets of plots plot_compare_timeseries(forecast_eval, type = "summary_score") plot_compare_timeseries(forecast_eval, type = "summary_score", score = "Bias" ) plot_compare_timeseries(forecast_eval, type = "horizon_score") plot_compare_timeseries(forecast_eval, type = "region_score") plot_compare_timeseries(forecast_eval, type = c("horizon_score", "region_score") ) ## End(Not run)
Plot a Forecast
plot_forecast( forecast = NULL, observations = NULL, horizon_cutoff = NULL, obs_cutoff_at_forecast = TRUE )
plot_forecast( forecast = NULL, observations = NULL, horizon_cutoff = NULL, obs_cutoff_at_forecast = TRUE )
forecast |
A dataframe with summarised forecasts as produced
by |
observations |
A dataframe of observations containing the following variables:
|
horizon_cutoff |
Numeric, defaults to NULL. Forecast horizon to plot up to. |
obs_cutoff_at_forecast |
Logical defaults to |
A ggplot2
object
## Not run: ## Forecast an Rt sample samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 21, samples = 10 ) ## Summarise forecast summarised_forecast <- summarise_forecast(samples) ## Plot forecast_cases plot_forecast(summarised_forecast, EpiSoon::example_obs_rts) ## Forecast a case sample pred_cases <- forecast_cases(EpiSoon::example_obs_cases, samples, serial_interval = EpiSoon::example_serial_interval ) summarised_case_forecast <- summarise_case_forecast(pred_cases) plot_forecast(summarised_case_forecast, EpiSoon::example_obs_cases) ## End(Not run)
## Not run: ## Forecast an Rt sample samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 21, samples = 10 ) ## Summarise forecast summarised_forecast <- summarise_forecast(samples) ## Plot forecast_cases plot_forecast(summarised_forecast, EpiSoon::example_obs_rts) ## Forecast a case sample pred_cases <- forecast_cases(EpiSoon::example_obs_cases, samples, serial_interval = EpiSoon::example_serial_interval ) summarised_case_forecast <- summarise_case_forecast(pred_cases) plot_forecast(summarised_case_forecast, EpiSoon::example_obs_cases) ## End(Not run)
Plot a Forecast
plot_forecast_evaluation( forecasts = NULL, observations = NULL, horizon_to_plot = 1 )
plot_forecast_evaluation( forecasts = NULL, observations = NULL, horizon_to_plot = 1 )
forecasts |
A dataframe as produced by |
observations |
A dataframe of observations containing the following variables:
|
horizon_to_plot |
Numeric vector, the forecast horizon to plot. |
A ggplot2
object
## Not run: ## Evaluate a model forecast_eval <- evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, serial_interval = EpiSoon::example_serial_interval, horizon = 7, samples = 10 ) ## Plot Rt forecast plot_forecast_evaluation(forecast_eval$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) ## Plot case forecast plot_forecast_evaluation(forecast_eval$forecast_cases, EpiSoon::example_obs_cases, horizon_to_plot = 7 ) ## End(Not run)
## Not run: ## Evaluate a model forecast_eval <- evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, serial_interval = EpiSoon::example_serial_interval, horizon = 7, samples = 10 ) ## Plot Rt forecast plot_forecast_evaluation(forecast_eval$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) ## Plot case forecast plot_forecast_evaluation(forecast_eval$forecast_cases, EpiSoon::example_obs_cases, horizon_to_plot = 7 ) ## End(Not run)
Plot forecast scores
plot_scores()
plot_scores()
A dataframe of summarised scores in a tidy format.
Predict cases for a single Rt sample forecasts
predict_cases( cases = NULL, rts = NULL, serial_interval = NULL, forecast_date = NULL, horizon = NULL, rdist = NULL )
predict_cases( cases = NULL, rts = NULL, serial_interval = NULL, forecast_date = NULL, horizon = NULL, rdist = NULL )
cases |
A dataframe containing |
rts |
A dataframe of containing two variables |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
forecast_date |
A character string date (format "yyyy-mm-dd") indicating
the forecast date. Defaults to |
horizon |
Numeric, the time horizon over which to predict. |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
Forecast cases for over a future forecast horizon
forecast <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 1 ) purrr::map_dfr(1:100, ~ predict_cases( cases = EpiSoon::example_obs_cases, rts = forecast, forecast_date = as.Date("2020-03-10"), serial_interval = example_serial_interval )) %>% dplyr::group_by(date) %>% dplyr::summarise(cases = mean(cases))
forecast <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 1 ) purrr::map_dfr(1:100, ~ predict_cases( cases = EpiSoon::example_obs_cases, rts = forecast, forecast_date = as.Date("2020-03-10"), serial_interval = example_serial_interval )) %>% dplyr::group_by(date) %>% dplyr::summarise(cases = mean(cases))
Predict cases for Rts based on observed data
predict_current_cases( cases = NULL, rts = NULL, serial_interval = NULL, rdist = NULL )
predict_current_cases( cases = NULL, rts = NULL, serial_interval = NULL, rdist = NULL )
cases |
A dataframe containing |
rts |
A dataframe of containing two variables |
serial_interval |
A numeric vector describing the probability distribution the serial interval.
See |
rdist |
A function to be used to sample the number of cases. Must take two
arguments with the first specfying the number of samples and the second the mean. Defaults
to |
Forecast cases for the current timestep
purrr::map_dfr(1:100, ~ predict_current_cases( cases = EpiSoon::example_obs_cases, rts = EpiSoon::example_obs_rts, serial_interval = EpiSoon::example_serial_interval )) %>% dplyr::group_by(date) %>% dplyr::summarise(cases = mean(cases))
purrr::map_dfr(1:100, ~ predict_current_cases( cases = EpiSoon::example_obs_cases, rts = EpiSoon::example_obs_rts, serial_interval = EpiSoon::example_serial_interval )) %>% dplyr::group_by(date) %>% dplyr::summarise(cases = mean(cases))
Score a case forecast
score_case_forecast(pred_cases, obs_cases, scores = "all")
score_case_forecast(pred_cases, obs_cases, scores = "all")
pred_cases |
Dataframe of predicted cases with the following variables: |
obs_cases |
Dataframe of observed cases with the following variables: |
scores |
Character vector defaulting to "all". Select which scores to return, default is all scores but any subset can be returned. |
A dataframe containing the following scores per forecast timepoint: dss, crps, logs, bias, and sharpness as well as the forecast date and time horizon.
## Not run: ## Fit a model (using a subset of observations) samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) pred_cases <- forecast_cases( EpiSoon::example_obs_cases, samples, EpiSoon::example_serial_interval ) ## Score the model fit (with observations during the time horizon of the forecast) score_case_forecast(pred_cases, EpiSoon::example_obs_cases) ## Score the model fit (with observations during the time horizon of the forecast) score_case_forecast(pred_cases, EpiSoon::example_obs_cases, scores = c("crps", "sharpness", "bias")) ## End(Not run)
## Not run: ## Fit a model (using a subset of observations) samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) pred_cases <- forecast_cases( EpiSoon::example_obs_cases, samples, EpiSoon::example_serial_interval ) ## Score the model fit (with observations during the time horizon of the forecast) score_case_forecast(pred_cases, EpiSoon::example_obs_cases) ## Score the model fit (with observations during the time horizon of the forecast) score_case_forecast(pred_cases, EpiSoon::example_obs_cases, scores = c("crps", "sharpness", "bias")) ## End(Not run)
Score a Model Fit
score_forecast(fit_samples, observations, scores = "all")
score_forecast(fit_samples, observations, scores = "all")
fit_samples |
A dataframe as produced by |
observations |
A dataframe of observations against which to score. Should contain a |
scores |
Character vector defaulting to "all". Select which scores to return, default is all scores but any subset can be returned. |
A dataframe containing the following scores per forecast timepoint: dss, crps, logs, bias, and sharpness as well as the forecast date and time horizon.
## Not run: ## Fit a model (using a subset of observations) samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Score the model fit (with observations during the time horizon of the forecast) score_forecast(samples, EpiSoon::example_obs_rts) ## Return just CRPS, bias and sharpness score_forecast(samples, EpiSoon::example_obs_rts, scores = c("crps", "sharpness", "bias")) ## Return just the CRPS score_forecast(samples, EpiSoon::example_obs_rts, scores = "crps") ## End(Not run)
## Not run: ## Fit a model (using a subset of observations) samples <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Score the model fit (with observations during the time horizon of the forecast) score_forecast(samples, EpiSoon::example_obs_rts) ## Return just CRPS, bias and sharpness score_forecast(samples, EpiSoon::example_obs_rts, scores = c("crps", "sharpness", "bias")) ## Return just the CRPS score_forecast(samples, EpiSoon::example_obs_rts, scores = "crps") ## End(Not run)
Provides a wrapper for different EpiSoon model wrappers and generates a mixture model of these models based on the (Continuous) Rank Probability Score
A list of models is supplied. These models are fit to the data up until a
period of observations of size weighting_period
. Forecasts are generated
from all the models for all time points in the weighting_period
.
Predictive samples generated by the individual models are then used to
create model weights in an ensemble based on CRPS. All models are then
refitted for the entire timeseries and predictions are generated from these
models. Draws from the individual model predictive samples are then used
to generate a mixture model with the weights obtained in the previous step.
The weights are computed using crps_weights
from
the package stackr
to minimise CRPS. The function
mixture_from_samples
from the same package is used
to draw samples from the
individual models to form the mixture models.
stackr_model( y = NULL, models = NULL, samples = NULL, horizon = NULL, weighting_period = 5, verbose = TRUE )
stackr_model( y = NULL, models = NULL, samples = NULL, horizon = NULL, weighting_period = 5, verbose = TRUE )
y |
Numeric vector of time points to forecast |
models |
A list of models. Models must be analogous to the form
|
samples |
Numeric, number of samples to take. |
horizon |
Numeric, the time horizon over which to predict. |
weighting_period |
The number of most recent timepoints to hold out to generate the weights for the mixture model |
verbose |
if TRUE, gives a message if number of observations is too small to do crps weighting |
A dataframe of predictions (with columns representing the time horizon and rows representing samples).
## Not run: # make list with models models <- list( "ARIMA" = function(...) { EpiSoon::fable_model(model = fable::ARIMA(y), ...) }, "ETS" = function(...) { EpiSoon::fable_model(model = fable::ETS(y), ...) }, "Drift" = function(...) { EpiSoon::fable_model(model = fable::RW(y ~ drift()), ...) } ) # make forecast on its own forecast <- stackr_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, models = models, samples = 10, horizon = 7, weighting_period = 5 ) # together with forecast_rt fc_rt <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { stackr_model( models = models, weighting_period = 5, ... ) }, samples = 10, horizon = 7 ) forecast_eval <- evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { stackr_model( models = models, weighting_period = 5, ... ) }, horizon = 7, samples = 10, serial_interval = example_serial_interval, min_points = 10 ) plot_forecast_evaluation(forecast_eval$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) ## End(Not run)
## Not run: # make list with models models <- list( "ARIMA" = function(...) { EpiSoon::fable_model(model = fable::ARIMA(y), ...) }, "ETS" = function(...) { EpiSoon::fable_model(model = fable::ETS(y), ...) }, "Drift" = function(...) { EpiSoon::fable_model(model = fable::RW(y ~ drift()), ...) } ) # make forecast on its own forecast <- stackr_model( y = EpiSoon::example_obs_rts[1:10, ]$rt, models = models, samples = 10, horizon = 7, weighting_period = 5 ) # together with forecast_rt fc_rt <- forecast_rt(EpiSoon::example_obs_rts[1:10, ], model = function(...) { stackr_model( models = models, weighting_period = 5, ... ) }, samples = 10, horizon = 7 ) forecast_eval <- evaluate_model(EpiSoon::example_obs_rts, EpiSoon::example_obs_cases, model = function(...) { stackr_model( models = models, weighting_period = 5, ... ) }, horizon = 7, samples = 10, serial_interval = example_serial_interval, min_points = 10 ) plot_forecast_evaluation(forecast_eval$forecast_rts, EpiSoon::example_obs_rts, horizon_to_plot = 7 ) ## End(Not run)
Summarise Forecast Cases
summarise_case_forecast(pred_cases)
summarise_case_forecast(pred_cases)
pred_cases |
A dataframe as produced by |
A summarised dataframe.
## Not run: ## Example forecast forecast <- forecast_rt(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Forecast cases case_forecast <- forecast_cases( EpiSoon::example_obs_cases, forecast, EpiSoon::example_serial_interval ) ## Summarise case forecast summarise_case_forecast(case_forecast) ## End(Not run)
## Not run: ## Example forecast forecast <- forecast_rt(EpiSoon::example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) ## Forecast cases case_forecast <- forecast_cases( EpiSoon::example_obs_cases, forecast, EpiSoon::example_serial_interval ) ## Summarise case forecast summarise_case_forecast(case_forecast) ## End(Not run)
Summarise Forecast Rts
summarise_forecast(fit_samples)
summarise_forecast(fit_samples)
fit_samples |
A dataframe as produced by |
A summarised dataframe.
## Not run: samples <- forecast_rt(example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) summarise_forecast(samples) ## End(Not run)
## Not run: samples <- forecast_rt(example_obs_rts, model = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) }, horizon = 7, samples = 10 ) summarise_forecast(samples) ## End(Not run)
Summarise model forecasting scores
summarise_scores(scores, variables = NULL, sel_scores = NULL)
summarise_scores(scores, variables = NULL, sel_scores = NULL)
scores |
A dataframe of model scores as produced by |
variables |
A character vector of variables names to group over. By default score type and model is grouped over if present. |
sel_scores |
A character vector indicating which scores to return information on. Defaults to all scores |
A dataframe of summarised scores in a tidy format.
## Not run: ## Example cases cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) ## Example Rts rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) } ) ## Compare models evaluations <- compare_timeseries(rts, cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Score across the default groups summarise_scores(evaluations$rt_scores) ## Also summarise across time horizon summarise_scores(evaluations$rt_scores, "horizon", sel_scores = "crps") ## Instead summarise across region and summarise case scores summarise_scores(evaluations$case_scores, "timeseries", sel_scores = "logs") ## End(Not run)
## Not run: ## Example cases cases <- EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_cases %>% dplyr::mutate(timeseries = "Region 2")) ## Example Rts rts <- EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 1") %>% dplyr::bind_rows(EpiSoon::example_obs_rts %>% dplyr::mutate(timeseries = "Region 2")) ## List of forecasting bsts models wrapped in functions. models <- list( "AR 3" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddAr(ss, y = y, lags = 3) }, ... ) }, "Semi-local linear trend" = function(...) { EpiSoon::bsts_model( model = function(ss, y) { bsts::AddSemilocalLinearTrend(ss, y = y) }, ... ) } ) ## Compare models evaluations <- compare_timeseries(rts, cases, models, horizon = 7, samples = 10, serial_interval = example_serial_interval ) ## Score across the default groups summarise_scores(evaluations$rt_scores) ## Also summarise across time horizon summarise_scores(evaluations$rt_scores, "horizon", sel_scores = "crps") ## Instead summarise across region and summarise case scores summarise_scores(evaluations$case_scores, "timeseries", sel_scores = "logs") ## End(Not run)