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Prediction layer for postprocessing

Source: R/layer_predict.R
layer_predict.Rd

Implements prediction on a fitted epi_workflow. One may want different types of prediction, and to potentially apply this after some amount of postprocessing. This would typically be the first layer in a frosting postprocessor.

Usage

layer_predict(
  frosting,
  type = NULL,
  opts = list(),
  ...,
  id = rand_id("predict_default")
)

Arguments

frosting

a frosting object

type

A single character value or NULL. Possible values are "numeric", "class", "prob", "conf_int", "pred_int", "quantile", "time", "hazard", "survival", or "raw". When NULL, predict() will choose an appropriate value based on the model's mode.

opts

A list of optional arguments to the underlying predict function that will be used when type = "raw". The list should not include options for the model object or the new data being predicted.

...

Additional parsnip-related options, depending on the value of type. Arguments to the underlying model's prediction function cannot be passed here (use the opts argument instead). Possible arguments are:

  • interval: for type equal to "survival" or "quantile", should interval estimates be added, if available? Options are "none" and "confidence".

  • level: for type equal to "conf_int", "pred_int", or "survival", this is the parameter for the tail area of the intervals (e.g. confidence level for confidence intervals). Default value is 0.95.

  • std_error: for type equal to "conf_int" or "pred_int", add the standard error of fit or prediction (on the scale of the linear predictors). Default value is FALSE.

  • quantile: for type equal to quantile, the quantiles of the distribution. Default is (1:9)/10.

  • eval_time: for type equal to "survival" or "hazard", the time points at which the survival probability or hazard is estimated.

id

a string identifying the layer

Value

An updated frosting object

See also

parsnip::predict.model_fit()

Examples

library(dplyr)
jhu <- case_death_rate_subset %>%
  filter(time_value > "2021-11-01", geo_value %in% c("ak", "ca", "ny"))

r <- epi_recipe(jhu) %>%
  step_epi_lag(death_rate, lag = c(0, 7, 14)) %>%
  step_epi_ahead(death_rate, ahead = 7) %>%
  step_epi_naomit()

wf <- epi_workflow(r, linear_reg()) %>% fit(jhu)
latest <- jhu %>% filter(time_value >= max(time_value) - 14)

# Predict layer alone
f <- frosting() %>% layer_predict()
wf1 <- wf %>% add_frosting(f)

p1 <- predict(wf1, latest)
p1
#> An `epi_df` object, 3 x 3 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 3
#>   geo_value time_value .pred
#> * <chr>     <date>     <dbl>
#> 1 ak        2021-12-31 0.245
#> 2 ca        2021-12-31 0.313
#> 3 ny        2021-12-31 0.295

# Prediction with interval
f <- frosting() %>% layer_predict(type = "pred_int")
wf2 <- wf %>% add_frosting(f)

p2 <- predict(wf2, latest)
p2
#> An `epi_df` object, 3 x 4 with metadata:
#> * geo_type  = state
#> * time_type = day
#> * as_of     = 2022-05-31 19:08:25.791826
#> 
#> # A tibble: 3 × 4
#>   geo_value time_value .pred_lower .pred_upper
#> * <chr>     <date>           <dbl>       <dbl>
#> 1 ak        2021-12-31      -0.366       0.856
#> 2 ca        2021-12-31      -0.284       0.910
#> 3 ny        2021-12-31      -0.301       0.891